{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nPP6JC6T4dOz",
    "outputId": "8d806822-0049-4522-f724-fa9be54f0bbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "4/1AY0e-g5AiNATbrHvfILXAXXxTvjhmw8TIRJJ7KCWa3Xa972_4xHuwu5t7bM\n",
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount(\"/content/gdrive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102,
     "referenced_widgets": [
      "c242f624dd954daa93ec22e16eef4ad9",
      "ac6d43c3f1704e8983e48621f98a4dd4",
      "4b7952f940e844049dfbcbcf14d2d05e",
      "1e41c216244f4b479a1af5ad605d5477",
      "f5f1f1b35c58494994cebea6b6c7b405",
      "3f9ae3af35c942ebb44c44f2e3372a52",
      "b39925ed4ea04caaadf2f9e6cec168fd",
      "ee180e20aa414f9992e9f41d47530604"
     ]
    },
    "id": "Rra-pXeG9ayO",
    "outputId": "9e79cb83-9d08-40fb-ed03-8ad43aa20b45"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c242f624dd954daa93ec22e16eef4ad9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import string\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import os\n",
    "from pickle import dump, load\n",
    "import numpy as np\n",
    "\n",
    "from keras.applications.xception import Xception, preprocess_input\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers.merge import add\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Dense, LSTM, Embedding, Dropout\n",
    "\n",
    "# small library for seeing the progress of loops.\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "tqdm().pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LGPa8JJxJp1U",
    "outputId": "b96a3e34-b104-4eda-abd3-bdcb03064433"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found GPU at: /device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name != '/device:GPU:0':\n",
    "  raise SystemError('GPU device not found')\n",
    "print('Found GPU at: {}'.format(device_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "sSImDtd79u5k"
   },
   "outputs": [],
   "source": [
    "# Loading a text file into memory\n",
    "def load_doc(filename):\n",
    "    # Opening the file as read only\n",
    "    file = open(filename, 'r')\n",
    "    text = file.read()\n",
    "    file.close()\n",
    "    return text\n",
    "\n",
    "# get all imgs with their captions\n",
    "def all_img_captions(filename):\n",
    "    file = load_doc(filename)\n",
    "    captions = file.split('\\n')\n",
    "    descriptions ={}\n",
    "    for caption in captions[:-1]:\n",
    "        img, caption = caption.split('\\t')\n",
    "        if img[:-2] not in descriptions:\n",
    "            descriptions[img[:-2]] = [caption]\n",
    "        else:\n",
    "            descriptions[img[:-2]].append(caption)\n",
    "    return descriptions\n",
    "\n",
    "##Data cleaning- lower casing, removing puntuations and words containing numbers\n",
    "def cleaning_text(captions):\n",
    "    table = str.maketrans('','',string.punctuation)\n",
    "    for img,caps in captions.items():\n",
    "        for i,img_caption in enumerate(caps):\n",
    "\n",
    "            img_caption.replace(\"-\",\" \")\n",
    "            desc = img_caption.split()\n",
    "\n",
    "            #converts to lower case\n",
    "            desc = [word.lower() for word in desc]\n",
    "            #remove punctuation from each token\n",
    "            desc = [word.translate(table) for word in desc]\n",
    "            #remove hanging 's and a \n",
    "            desc = [word for word in desc if(len(word)>1)]\n",
    "            #remove tokens with numbers in them\n",
    "            desc = [word for word in desc if(word.isalpha())]\n",
    "            #convert back to string\n",
    "\n",
    "            img_caption = ' '.join(desc)\n",
    "            captions[img][i]= img_caption\n",
    "    return captions\n",
    "\n",
    "def text_vocabulary(descriptions):\n",
    "    # build vocabulary of all unique words\n",
    "    vocab = set()\n",
    "    \n",
    "    for key in descriptions.keys():\n",
    "        [vocab.update(d.split()) for d in descriptions[key]]\n",
    "    \n",
    "    return vocab\n",
    "\n",
    "#All descriptions in one file \n",
    "def save_descriptions(descriptions, filename):\n",
    "    lines = list()\n",
    "    for key, desc_list in descriptions.items():\n",
    "        for desc in desc_list:\n",
    "            lines.append(key + '\\t' + desc )\n",
    "    data = \"\\n\".join(lines)\n",
    "    file = open(filename,\"w\")\n",
    "    file.write(data)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "els217F_-CLY",
    "outputId": "769f54a5-df27-4aa5-e476-00bcf0e06f5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of descriptions = 8092\n",
      "Length of vocabulary =  8763\n"
     ]
    }
   ],
   "source": [
    "# Set these path according to project folder in you system\n",
    "dataset_text = \"/content/gdrive/MyDrive/Flickr8k_text\"\n",
    "dataset_images = \"/content/gdrive/MyDrive/Flickr8k_Dataset/img\"\n",
    "\n",
    "#we prepare our text data\n",
    "filename = dataset_text + \"/\" + \"Flickr8k.token.txt\"\n",
    "#loading the file that contains all data\n",
    "#mapping them into descriptions dictionary img to 5 captions\n",
    "descriptions = all_img_captions(filename)\n",
    "print(\"Length of descriptions =\" ,len(descriptions))\n",
    "\n",
    "#cleaning the descriptions\n",
    "clean_descriptions = cleaning_text(descriptions)\n",
    "\n",
    "#building vocabulary \n",
    "vocabulary = text_vocabulary(clean_descriptions)\n",
    "print(\"Length of vocabulary = \", len(vocabulary))\n",
    "\n",
    "#saving each description to file \n",
    "save_descriptions(clean_descriptions, \"descriptions.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 169,
     "referenced_widgets": [
      "dec879e0de2544e5aef9cf1536f68818",
      "2a9286b42e3647189ea4f8b874d17b8c",
      "17a4867f0f6141cd82fbf183dc4214dc",
      "a4ca2abdd9b3460ea829b10b5edbcf20",
      "b890dcc41cc74ea3b2ddddf9d862cd96",
      "b22cc94769054f528af11a6d7d88840b",
      "11cfc6a710404fa686149f7f4ffb7388",
      "c5d8893bfe13409ba32cc91822fffc6b"
     ]
    },
    "id": "6huIQ2VsEaE5",
    "outputId": "07cba0a9-555a-4882-c37e-4c052bf33a21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "83689472/83683744 [==============================] - 0s 0us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dec879e0de2544e5aef9cf1536f68818",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=8111.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def extract_features(directory):\n",
    "    with tf.device('/device:GPU:0'):\n",
    "        model = Xception( include_top=False, pooling='avg' )\n",
    "        features = {}\n",
    "        for img in tqdm(os.listdir(directory)):\n",
    "            filename = directory + \"/\" + img\n",
    "            image = Image.open(filename)\n",
    "            image = image.resize((299,299))\n",
    "            image = np.expand_dims(image, axis=0)\n",
    "            #image = preprocess_input(image)\n",
    "            image = image/127.5\n",
    "            image = image - 1.0\n",
    "\n",
    "            feature = model.predict(image)\n",
    "            features[img] = feature\n",
    "        return features\n",
    "\n",
    "#2048 feature vector\n",
    "features = extract_features(dataset_images)\n",
    "dump(features, open(\"features.p\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4F6qSqbvrUax"
   },
   "outputs": [],
   "source": [
    "# features = load(open(\"features.p\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "uckkh5H6E3ka"
   },
   "outputs": [],
   "source": [
    "#load the data \n",
    "def load_photos(filename):\n",
    "    file = load_doc(filename)\n",
    "    photos = file.split(\"\\n\")[:-1]\n",
    "    return photos\n",
    "\n",
    "\n",
    "def load_clean_descriptions(filename, photos): \n",
    "    #loading clean_descriptions\n",
    "    file = load_doc(filename)\n",
    "    descriptions = {}\n",
    "    for line in file.split(\"\\n\"):\n",
    "\n",
    "        words = line.split()\n",
    "        if len(words)<1 :\n",
    "            continue\n",
    "\n",
    "        image, image_caption = words[0], words[1:]\n",
    "\n",
    "        if image in photos:\n",
    "            if image not in descriptions:\n",
    "                descriptions[image] = []\n",
    "            desc = '<start> ' + \" \".join(image_caption) + ' <end>'\n",
    "            descriptions[image].append(desc)\n",
    "\n",
    "    return descriptions\n",
    "\n",
    "\n",
    "def load_features(photos):\n",
    "    #loading all features\n",
    "    all_features = load(open(\"features.p\",\"rb\"))\n",
    "    #selecting only needed features\n",
    "    features = {k:all_features[k] for k in photos}\n",
    "    return features\n",
    "\n",
    "\n",
    "filename = dataset_text + \"/\" + \"Flickr_8k.trainImages.txt\"\n",
    "\n",
    "#train = loading_data(filename)\n",
    "train_imgs = load_photos(filename)\n",
    "train_descriptions = load_clean_descriptions(\"descriptions.txt\", train_imgs)\n",
    "train_features = load_features(train_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EOui7MMmXMbd",
    "outputId": "50378fac-af1f-4bff-bca5-9b4008a29fe9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7577"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting dictionary to clean list of descriptions\n",
    "def dict_to_list(descriptions):\n",
    "    all_desc = []\n",
    "    for key in descriptions.keys():\n",
    "        [all_desc.append(d) for d in descriptions[key]]\n",
    "    return all_desc\n",
    "\n",
    "#creating tokenizer class \n",
    "#this will vectorise text corpus\n",
    "#each integer will represent token in dictionary\n",
    "\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "\n",
    "def create_tokenizer(descriptions):\n",
    "    desc_list = dict_to_list(descriptions)\n",
    "    tokenizer = Tokenizer()\n",
    "    tokenizer.fit_on_texts(desc_list)\n",
    "    return tokenizer\n",
    "\n",
    "# give each word an index, and store that into tokenizer.p pickle file\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "dump(tokenizer, open('tokenizer.p', 'wb'))\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "892eJqCDXQ-9",
    "outputId": "0094a7bc-0133-44c6-e7b4-f862118c50e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate maximum length of descriptions\n",
    "def max_length(descriptions):\n",
    "    desc_list = dict_to_list(descriptions)\n",
    "    return max(len(d.split()) for d in desc_list)\n",
    "    \n",
    "max_length = max_length(descriptions)\n",
    "max_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MqpiJ6yzXX0l",
    "outputId": "6205b044-87a9-44d6-a387-238c2bdde800"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((47, 2048), (47, 32), (47, 7577))"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features['1000268201_693b08cb0e.jpg'][0]\n",
    "\n",
    "#data generator, used by model.fit_generator()\n",
    "\n",
    "#data generator, used by model.fit_generator()\n",
    "def data_generator(descriptions, features, tokenizer, max_length):\n",
    "    while 1:\n",
    "        for key, description_list in descriptions.items():\n",
    "            #retrieve photo features\n",
    "            feature = features[key][0]\n",
    "            input_image, input_sequence, output_word = create_sequences(tokenizer, max_length, description_list, feature)\n",
    "            yield [input_image, input_sequence], output_word         \n",
    "\n",
    "def create_sequences(tokenizer, max_length, desc_list, feature):\n",
    "    X1, X2, y = list(), list(), list()\n",
    "    # walk through each description for the image\n",
    "    for desc in desc_list:\n",
    "        # encode the sequence\n",
    "        seq = tokenizer.texts_to_sequences([desc])[0]\n",
    "        # split one sequence into multiple X,y pairs\n",
    "        for i in range(1, len(seq)):\n",
    "            # split into input and output pair\n",
    "            in_seq, out_seq = seq[:i], seq[i]\n",
    "            # pad input sequence\n",
    "            in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n",
    "            # encode output sequence\n",
    "            out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n",
    "            # store\n",
    "            X1.append(feature)\n",
    "            X2.append(in_seq)\n",
    "            y.append(out_seq)\n",
    "    return np.array(X1), np.array(X2), np.array(y)\n",
    "\n",
    "#You can check the shape of the input and output for your model\n",
    "[a,b],c = next(data_generator(train_descriptions, features, tokenizer, max_length))\n",
    "a.shape, b.shape, c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "PT_JERzbXhZR"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from keras.utils import plot_model\n",
    "with tf.device('/device:GPU:0'):\n",
    "# define the captioning model\n",
    "  def define_model(vocab_size, max_length):\n",
    "      \n",
    "      # features from the CNN model squeezed from 2048 to 256 nodes\n",
    "      inputs1 = Input(shape=(2048,))\n",
    "      fe1 = Dropout(0.5)(inputs1)\n",
    "      fe2 = Dense(256, activation='relu')(fe1)\n",
    "\n",
    "      # LSTM sequence model\n",
    "      inputs2 = Input(shape=(max_length,))\n",
    "      se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)\n",
    "      se2 = Dropout(0.5)(se1)\n",
    "      se3 = LSTM(256)(se2)\n",
    "\n",
    "      # Merging both models\n",
    "      decoder1 = add([fe2, se3])\n",
    "      decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "      outputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
    "      \n",
    "      # tie it together [image, seq] [word]\n",
    "      model = Model(inputs=[inputs1, inputs2], outputs=outputs)\n",
    "      model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "      \n",
    "      # summarize model\n",
    "      print(model.summary())\n",
    "      plot_model(model, to_file='model.png', show_shapes=True)\n",
    "      \n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zcvSmMopXqq7",
    "outputId": "27098f3e-3d74-4a25-855c-9b40a370d701"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:  6000\n",
      "Descriptions: train= 6000\n",
      "Photos: train= 6000\n",
      "Vocabulary Size: 7577\n",
      "Description Length:  32\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 32)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, 2048)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 32, 256)      1939712     input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 2048)         0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 32, 256)      0           embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          524544      dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     (None, 256)          525312      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 256)          0           dense[0][0]                      \n",
      "                                                                 lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          65792       add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 7577)         1947289     dense_1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 5,002,649\n",
      "Trainable params: 5,002,649\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "6000/6000 [==============================] - 601s 100ms/step - loss: 4.9934\n",
      "6000/6000 [==============================] - 601s 100ms/step - loss: 3.6519\n",
      "6000/6000 [==============================] - 601s 100ms/step - loss: 3.3666\n",
      "6000/6000 [==============================] - 604s 101ms/step - loss: 3.1980\n",
      "6000/6000 [==============================] - 604s 101ms/step - loss: 3.0777\n",
      "6000/6000 [==============================] - 602s 100ms/step - loss: 2.9913\n",
      "6000/6000 [==============================] - 598s 100ms/step - loss: 2.9217\n",
      "6000/6000 [==============================] - 603s 101ms/step - loss: 2.8663\n",
      "6000/6000 [==============================] - 611s 102ms/step - loss: 2.8180\n",
      "6000/6000 [==============================] - 615s 103ms/step - loss: 2.7886\n"
     ]
    }
   ],
   "source": [
    "print('Dataset: ', len(train_imgs))\n",
    "print('Descriptions: train=', len(train_descriptions))\n",
    "print('Photos: train=', len(train_features))\n",
    "print('Vocabulary Size:', vocab_size)\n",
    "print('Description Length: ', max_length)\n",
    "\n",
    "model = define_model(vocab_size, max_length)\n",
    "epochs = 10\n",
    "steps = len(train_descriptions)\n",
    "os.mkdir(\"models6\")\n",
    "for i in range(epochs):\n",
    "    generator = data_generator(train_descriptions, train_features, tokenizer, max_length)\n",
    "    model.fit(generator, epochs=1, steps_per_epoch= steps, verbose=1)\n",
    "    model.save(\"models6/model_\" + str(i) + \".h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 353
    },
    "id": "3SfHnCnhX02m",
    "outputId": "dff1b585-33cf-4c34-8db4-f006ba517708"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "start black and white dog is running through the grass end\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6fa44ab080>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p8vLtxDk1StJ8/ryRSuL5POE0BOsZLwPl+YJRBiOWx7YTw8E6pk0Q4wghYk+WViDljnThdJ55LOnIbohHYQ/SSWkEuN8WeggMgwVtEGms+4Z3EYVlHD1DHI8APMKWIW0NkUhOwuvXN2jw3XefOM0RaxUix2PuNSGmEUaPNRqlPb11lBye4sFoKn58e+P5dOI0TdzXjXkamD9ErFOUnvnyuvL97x8EZwkhoK3h8jRjMJymE9Iq4aTQtlJL4bFk9v1I407zRG9CTgnrDY2M95Z5vpBSp7ROqxX9nv6dzxc0B+GVc6HUQpeK1M55nqi2kZL6SXz9IkBurOJyObFthRgPUK+PhBLN07PHWcd0CrS+k0vjdI7M84ixCloD3fGDxRhoreGDx/lCrha6JURHlcxW7nRpvL7e2beV8+mC9RptFM578pb5+vVGrhX3ZKi5Mgwj//AffqDWwjmMtAI/XD+j0Ygy5App28j1gZtgmh1KV07vueHaKnTFWVvWR2HdMgaL15pahVwz+1bxXlFrZlsS3np6E2KwoDVdFHR3yBLQNHamKeCCYLRiDpqXJ83b642Xl5m0ZbxTpLTx5cuDUjrGGs7nEVGG3hp+cIwnT06Z/VGZzxO5rmgVSEvhd99/JcwLusM5jtw+rwyj4229Ybzi5eVM2htKa6xT5LTSszD4id41j68rdhRSSvQuWBuIUVFqZ3t0pGmMb2jXkQl8UEhvB7ewN05PZ7otSGt0LLkJ2ipQME9nrDFYrVDRYexPE0K/CJBLV6xL53q9cX6aOD07nr7xKAVDGKAJ1oHkjjNCd50sN3Tt0A4NhiqOWkBpzfK4sy4HweF9Be1x2oDS1F5xDpyfKCVTd8fjtmOURehcbzvD5NjXhMGx3RPeaVqv/MvPvyX4ET9MpFQpOYNohhCpfaOUjVIbfnTU3Hn9eqVWRfQD0R+uwb5lpHaWW0Vrw9fXlVoK3356JsaINY09JUrbqS2jjTDEmdYKrQndOUL0hMFS8krLAjSEyjh4vHfop4FWhW3NDMOAUpnaKtu+gAi9W6TbA/ymU3vBesv6WLHW8PLhhT0XDs5JqD1ho0VpTbCRvWw8feMYTxbvNa0X8q7IHW7XBaM0pw8TW91QOJ6fRpQWtB3AaiSDM57WOz9++cq2Vspe8GNgniObKux5B11o0rHaHFmhPeGcORQsvdEQpBqscj+Jr18EyFsX9jXTe6Xk/chm6IPuLSVBU1jtabXhBnNkYjhAorql5oOSRxStanozXL9kXLDEF0erh8Anl/3IuxqDdwdR1Kslb53bdaPWhhSLGRxDjEgHLRojYLXl6XLhsWW2/aCv17RitcU7Q22VVBK5NLRRWO2IYcLaI1/9uD/YUuZ2faOLYt8aY5woteK9RusjxhiniHaGL1+/8HbbGQfHh6fIsq1o4zA5oMzMnguIIu+V3ushQEMjUYE6RFC1NkI0+DigtcY7g1JwvW+8XX/kxbwQw/Fd42AJwwvWK6R14tkhQCtCK4pxdKy3zOUlMjuD8QfTaKyFZsEUXr6b6RfL43UnDo4pBmrNGHckElpP9N4Qa1mXFUH45uWJLWUEGCbPnje0VuRUUErhXcAZg7UGYyLynoGp0hEMdevs2+Mn8fWLADkCYbLEy4lgPeuS2ffEulSc89xeN2reGUPku7+Y6DXRVYGmKSlhOAgkqzylClUqH4cLCng5P9FKZ9k2tkUwFqo+fkSHSq8wzo4waLYtUYpFYVluCRc1w+SpecU7g+kakrCvO9pDCDDFgfv9cIOsOcBeSiY+RabTQfLUsiG2UVOi0yn5kDLk0phPkX27sW13xsEzDxNda5r8SO4NXRRf3w4yKJdM8J1eQKtjDIA4enrPlK3zm48v1FT5P3/7Pcqo43ullRg8Tg34aHk6TaiuyGmnpMw4xEOK0HaMVxhlQDe89+RVkbaGsYeLYwdFr2DNIeZa80pv6mBjc+X17RXbIz98vzFdHMp28iPjnON+X3DOHsF6NKyPhFUwmIFHXvn6+Qo0lBaG4DHWHZsPnZwW4hjw4UhMDN6z50xzjV7aT8LrFwJyoVYhOAM0hsG9SygbrVbGeSQtFa1h2xaitYjS0BUpZaQ3tq2jpeCcJcuDYXCM40QjUUUwxjCNZwyGr6+fUcCjFmo2eK8wxjLNEe8trcKednJd2PeCj4GiCrUcslFtOiHA+TRRcuH5m0gYhkPma6DT0MpiteHxuFFKp5SCd57nJ8fb24pzGu8D09nxoh26gzaKVhtLWhmmyMl7dBvI+wZK8XZ/5XLStLbzdH6GrvjyecU6IQSLN559OeIW5wMhGERBcB5njwBXo1ivGc/AfV+4LnecSTgCOWdy34kRnj9MTBfDvnYM8YgLEJQyeO+wTqONw7mR3jTbuuGs5dNfXOhNc3tN5JqRVrk9HtTaiGGiS0fU4RK6oKk5c7sVmq6gOjGOnC8DXRo5Z87TQC6KsmvqrjHW4qxGNUfNiSoFzP/P2pX/L9Z6Z70K21X45tNEY0e7jgZ8sGgcwRryntlrpe/mPfWUUUrjoiPtwuO2Q1PE+VDbLdsrSgRpULNBmmHwEzHO+FBoLhFHh1RPKYV5OGj+2hLowjge2otgB75+udGlgVGMs8V7xzyM6Lkeu3NPaGOoVah5B6CKpxfL6C3n6ZAYpJR4eo6UbBiiofQN6x1GWVpWtNYwujOoQNAXenGsy41G5vI8H9JioyAI88sA/kzdM9MUiD6g3JGLbiTCPOOcIcaRP2jIexPCZKl749/5yye+wdGK5vFZcTpFhuEDwQTWsqBRGNe5ftnQVrNlqG1gGAzQqbVSSke6wtpIrfnQymt4+uARMXQlxNmBUgQfUMqw55XeM3EayLZilTC7id4bdMX1eqf1grEa5yOiOqfniZwTOSXWrdBLwzgDBsY4/iS+fhkgr43//V/+FT4GsmReXiLOH+q5nBJ1PzImKVdSFrqCeZ4wNqJUppSNYbScxgvLdWc6RbQDHzVaHYKg69uCdMXvfvgBazRPOaCD8OnjxBgjKa+01g4defC0bg65gbVM40QcPKIg5Y2WO2lVrPeC1pm1bqSyo517l4BWem8M/owWR0lHQcUwWmKwTGOk5I6zCm1OFHa0U1gd2fZCYGBddh7XN6Kb+fDpmZw3ci2kfSeVyrqvpJqQI/fHtiX2rWCMYVt26l5YwkbpiSF6vNfM04mOgGl0W9mloX3HWuH8rUdXx3JdOZ0jouzhgg3C5ZOjpCPwlaZJqUNpKDmKSmprpO3tSPdqxfLYmKaIGTrDFHABaimULOTcyS2DrojiKFjpiVwb4xhx1uBK4PEQGgK9EOJw3EtgSQ/CFHiUBzXBOE3o/ieRQrS44Qhero83jJ8ZBk8ugtOWrjL7/iDGkVQh2kjaCqIrw9DZUwZV8BbcbJjOJ9b7wo8/vqG1xXvLp09njDZ888kjXbM+EjFEnPZ8//3vcd6z7xvaGJQBbSx5z4wxoJTCOo3guH69k/byLrsVrNO0CrUY0poIPmJNJQ6eGCa0KEoRlFikF0rp3NeFUgSjBaUrYfCcwoizFgmQa8ZbobOTu+Z8fmaeJ+7LDWmVbjXOOVLJSKk4bQmTp5ZC3jMhDHyYP3Dfrrzed9TTIWha9o3rdaHsQskd7TrxpEA1EM8YRsYPjsyO8oeAS4yluI4ylbRV7o8r83nAO8t6T8QYeL48k+LO/XGjKmE6DwTnyGWl5UM12pvGYtHScRpaNzzeDt3LNIwE71EItWVcDLh6SKfneSBvheX+wOiD/Gst0VWjVGF/e+Npvvwkvn4RINcaPn47U2rh/li43u/U7hmHAbHm2OVMQ0siPTL7Y6dJB9cxNjKeTtSayGmhLmDbhfuSuO0b82miSULZfgivqMQQGI1H9c59veIHj1IWpRy5JIINdDrGGzCKZVvpNAZ/4cPLd9xe7xjtGWMgtwfdNqbzTO+w3Ba+fnlwni7M44jWnZQTrVaGOCBdEVqktHyoKtcKTfB54nFfuK139rwRYiQGgw8WHzWqK6ZxIueM9MowWJQW7BBpvWCDBnNkMbT043QbPN+YF7zVtNKpWkhFk9aONRajBRD2/dAAtb5Qc6NXzRwn7G7Z8471ltr2A4QdUjFgDdteeNx23r4+jqomY+m64oPCeYWqA8pojGp4Z7BYjO6IHMFjneHLj6+0ZGnqkESIdMIE2nh0z0h7PzHE8cMPPzCfJ+IceLpESlXcrldS/RNgPLUxxFHhu8XamfP5iX0/gpWSC1AxxjL6Cc2hgMspk/edMIDWA9Z6zOBIVH7/5feAQltLk0KtidBHlLGIUrw9HuwpYxTE6IlDwGnDY13JW2O9gxvBx6MsDAyPZWEInTn0QyQWJrSCXDqP+4b3njh4Lk8z0zyQ9o376504RkqDXAsooSYwOKyzpFUou6bXztI6gqc1x/eff483C9/95iPQoRVKaSxLei/h67xcJt6uK7ULVhl6U1AsUhVrTmgycTDMk2GIE6kl6nst5nrfmE8D65Joj36U7rnDVXPBUXNhkYLcKgqDDIquFZ1EHCaUVShtyDnTGoQxsqeE0rDcN/plYnUJjSaEgCAU2XDK8/3v7mht+ObTB0QrLs/PqHYUx3gXaQLLkljTjtbQsoVmWFOmas192w5iyFictRgxqD8F7Urv9chGOM00BbTSlGLej6dI2jaCGZFquZwnlFZwg7akg3ApDWc1PiimaeDTtx95fftK74LSilqFVhs1N0QbRNwRNMlOV0IHhmjwk0cZoTbhm2+f3xWBnd4Mj+XOuqxsQZiCI7eFYCOttaMGUmn2LfFYH2inMMZRVsX9y0o2G8PJsW0rrR4PpBVhGp7o1VLa4deXVBDb+PThE/u6ktfGOJ6QZnl9fTvo+dwJQbjdF9K+07uitoxSip4aYxjxw0hXgK08ysa9bNTW2bedwc9cPoxEG9E2HFLaClMI+CBoYxiHidKEZhqWo8Didl0wToOt0DX7tmK8IyjL29c3rNfgIKdG+v2N02k4Tqso5LqR2pUhTIzjGemHgnFJC3tNzEOk945OjSoVMe9MaeuoYUTRaGTCYOm9s+2JVjemaWAcI979CTCef9BMaGWBo0ZzHC6o3nkentl0Yt0Wct8JUVGpPH0YePoQMNoirVNaYRg8rWXWdTuqi3rHOUvZC/elUZuiUnBecz5dCGHCukPgVWvDec148tj3dJvpndR2tDF895snbtcVbTJZZ0RpWstsa0YpYbAB5y1RRX788RVtDOfTCYnC0xyZnwK1WFJRLMvCXipf76+kvfHh6ZlpmtjUBiiGGHiOT2z7zu26EifFNI5I1yhd8bFjnUJEM/gT9+XBkhb0IGAqVjn2VI8SMTdQW6OlRNk15fFgjhPOaFqH89NIuwhOa57mE106bhxJdQWB9e3YrafzgPWdGDXKmEOPkja0aPa0MPuRcRiJoVPWTto7l5czftD4pondsNw3kPUgsTbHXgrKCo2KshplNcEE3m5vR1cG0YcWRzrP33xgPk8HDjbhcVvRpjOePWX/E3BXAKRlTIjUfDBdtWxIU/x++z3WGbTTPMUzxsHr7U5ZGiE4hjAyj4eORaG4Pzq9Q/AnBuvJqZD2ynQ6s+cVI5peCznl44jXHUThnMNoeLu+opRmCAPWHO4MqhKC4dOnAZSh5IpRGmsOTU3OCREwtnMaPeP0Gx6PHecs8SmgSShAmc5oA8GfOM2dtAnLUlFKcb2+Ef3A9ig8T2eeLhdKL6Se2Utl2xLLvvDtb86EoaO1UCeLZEUpHhctrW1EM1KSZhhG3u6vpLIQosVoOJ1GbLVMdiKODhUae098fr1iBo/4Q3bQiiFGj8FwbwvnlxPjeWLdH7R3jXpth1gqOMd4GhDT6GRs9IzTBP3Q4jSxaKux3TLNZ+BgnjvC69ev9J755uWZ8+WZLodOpXWFcZr54lgD9KZZ0kb5mnFWM48zvfUj07JZdP/p9kG/CJArFFo8r19unOYTcQg0abj3srDgB1qpKHWkD63zaNcZhki0AwpDXjIinZwqeduPUdWONMXHD9/S2VE20TrUAi/PTwRn6aqwbDs0qE2oBYRGb4VaElrMUcSsBe8t3jnmeTiktcCey5H2cgZjFM4NpAyXMGAtIAUlgVI3Xm+vjE96Mz4AACAASURBVP6Fx31lXTPGaIKdafkQOq37yg9f3sB2tHOEYNnTRhUorYCCz1+unC4KrSsIGDXgLCjpiBKME15f36APOGNwIdB6JqVG9BFjBGUPF6vRMUb47jen974tnZObub5tlE1T0lG0IHIUZbeaDhmzCH7QPPuRWgT6UYwh0rD60PKUVnm7vWJ2yzTO1Hr8yJVSDMOE0pZvv/uWbV9xwdFaY4gnPn/+iu6amjLFCiEe8gLLTM0VqRUth6DLzRNIo8qfABmktCZOI7kJynbu61dOp9NRfrUutFZABKMObXIc/Dugd3RTvG4b2yNR3gse3HCo97btgdEWYzS1bxgnCA2lDdfrDWuE02liGidowp4PKvt0HqA39j1TM+RUEK3YHw3xBqM6IgXj4XS+UNLOsjzQRvN0Ph8srGj2baO2O84dFfzTcMYYePlwwbmK4gA3UeMHgzRhevoHnM0Joy2PfSHXhHEOZy1pz9TS+eFf7RjXGYdADI1923h6nnEErFOcPjjWpfC4Vgan8YPm6fly9F2plXlytFyJw3hII9pGaRWo1JRJeyJvwnJfD1lDd2htmecJBEotXGZLrZ1t7exbZV8zQxhptXC7f6EKaKfwNhzuYzv6qKzbA2sVw+hRxhLDSG+ZpT4oTdA4pDaQRtoTw3ToVVJaCD5SEJIktINGojVh3/4EaP3eO61nnl5mnD/IBR+g5Ip0hTQI4WizkPZMrfWdIFBorxingVKF0o4+JI81HZX0RphPli0lWq8EZTHGM82n92BXaN2Sl872eBzaiOjZtpXH7c4QB87nE1qPiBj2qaO15Xp7o5SN2UX2tNFqZV13fNR8//mvSEkQ8XhniYOn9YaL4egiZQytdYytBG8ZphHRhnVZjl4pcaZLI9WE9Z41X1EipNoRgfM4Mz59ZN1uKG1Q4vjy+iPoxtPzjLWGYQiEqInjRs2F1hRa20OjjtBVwc+B0pZDhFUBUexpwSiLkUpOCWMsy75zboGuLL0WQGGt5X5dENGc5w+cZ831+hWlwIUz677QUqb1iraC5EoXaNmgGShlpS7LQeQYg/GaMUyAxSrLbX0vRwz2WDMNTTewQgiO+t6tjKbQ1uHdnwIZZDSXy0xKCaMU1gd6hzhEQohQhH1b6L1wOp3JqXG/35jGkZwLtRfiYInBMI0DrXbKrliWQ9n4WBecV5SyMI4DwR8+9vk0o8TQiuHT/C3SO9UW1nRD6URrltfr0VXLakevipQ3OoINHusdqKNHyel0IreK1g4nR5pRpB1E0B8zPBWkkkun5Mz13nHX5XB1nMF5g9iCA7a8sm3lIKG6puyJddkOuvubhjhBKdi3jWk848J7ezkg1wq9MwTF/PzCujb2bWc6n2m9sO+JVhS5rYTgmWeHMYZlebAsN7I0/BhY7opcG8u+4VeFtZ0hBJwLnM+HH+xsZ9sfzBdHS0fLNo1iGgZk9EfrOgz1kREaz09nxFhaL0cqUsl7oH8oN3EepSvX2ytDGHHOvitHO0YpUjlaVmhtUDg0Gmv/BKS2vTe27UbJmZQUzh9ZgV4b2mhM0EQTaV0jvSNUXj4+M8bIsizo1lG2v+9GV9AO40amySHGsaSFDy8fUPqQezqncFazLwtaeaId2ZedbUvc+yvjOTLOZ3rtCJpaK101Wu00UdRasVaxbTtKCRqLUYbcOt4fhRtaN6QfjbV619SaaCWj9QEoH2aqKdxvKzkvfPfdx0Ni2nawnfdEE0Y893ui7A2tHC1n3u5HVmIaL2hfOQ8ea46SvrTXY7PQFms0e9nw0aNNYN1viIC1HmmgmmF57GyPnfNpPsoO4wgqYUygUdHVg5JDoZkfDGFEcTyj8+lMjI4ujtY7Wgm1KoJyPJYbzimkdnTTPF9OqJPi26ePXPc3UkuAHKKwLbPcM9OkDt4iBnx8OQRl+mgStW+JVhrBeq7XBSWK8+WEtZaU8k/i6xcB8lob3//4ineBaRqOjlpFKGUjp4JTRw9C4xTzNNFb4XZ9o6SRaRjZm/D25Su1NbqGGCKnOR4V38Hy712+g6rZS3lvcZDJZSdthbQdWZYhXlDvXZmUGMqej05OJoBStJaZpgGrFOM4UEpGm/fmnl0hypO2DacMz/OZ+/pKVwYRhdUaF4S1NB5vG+MU8e/NlE7jRPae2irGBFLeubeVfavv6dEViuHLlze8dVzmAdngdX+wjpXzaYDkefy48fQyEC6ClkP22pohv92preBdPKqZMMwnf3Q8CBGITHN8bwUBlhNDHGnSMV4jfUApRSmJaXomOk9phS2vtOsb+74Dgtb6cC8bxCnQ8VhtaRnWNTO+k263xxtrOYJNbQFl0dYyqAlrIrVUaitYJzjj6NKoteK9x8RDymy9RaHxNqI1uD8Fd8U6x8fnb9nSSqmJOByNfEpJjINnfS3c3jJdCffwlZcPJ+yoUWh+98MPxwKYgaenkek0kutOa4VUKnnRWALROjSHO4Fo8pZ5+5zozVCyYF46L9/MOAeX85mUM15N/O6HH+k0YrRs6w4ieH8071TGH7p008lpwwV30OoGrHUIilYP12VbV+pumP0z5E5fNC8fntBa2OvKVhe6KJQyWOMp9UruhX075MBWDWxL4fPbZ14+zIznkWEAVCMOBvUyU+mQNBhL8BOtdF5frxgN8dkzDI7eodXMLe+0duz+zkVAs23rEQ95y54Lj8eDOT5xGk+Hf6yFbb9zOp1wXtNbJ5dC65V5HvHRU3NBA988f4e1gW1dmSc5KqfazqMmOppohXXbaFUBinkaka54vd1wwWG9JpeMQtHlkF3nnmm9vXf/NWxpw/v4tzSJ+4WAXAF/8c1fcF1eabqitBzpMXOwns/fBJ6eTkgzfPlyJZeEMe0Q4J9Hzuczzhqi9/8Xde8Oa1uWpWl98z3XWnvv87g3IjIyq6tLNEhIgI2LhAVCaq8lLECY4NMebrtISHgtaIeHBwYeEsLCwsACNQ1d1VUZmRFx7z1n770e840xdqZKTVcWoigpY0mhuHfHuWfHPWeeOccc4/+/n229opS0yKwL9Aa3tztmAlTHmkBwga4Hv1431lslRM3kHcEp1uMGZmCtx0bPh69e0EYsc4aAUZ7btjKUIfqZWjO17WK2jhHrNGUcYAb7urGvidfnr5imSFoPDIFgHM5WrBXchDEKi7je15t4IgUumrmviTmecSfPV7/4mtIrf/Zn/4SuBgNDjAZ3Bnc21DKoXe4rp+VMdJ4pLNAUrSpSLiil+fzjG9s9UXZ4ejrx4asTrWq0Wvj+138iDqXTwhQvRD9D61iv0N7w/PQNrReiCbTWKEemNY23TrpRTWOrJtOlA1blkhjniVoTxhiGskDhYk+o4ailc/28USqU0giPy3pthVaabChKS9u2NU7TEwoj2vQOXv8E4EJKKX59/VNKzUyTKPfoWrDJVcT4WMV9O3CzZp4j2/FOawl04O3tC9477toyRhZQ5Wji4awVGzR1PJgqYaC0Bd35W//i1/SsqCUTomNyAVTFxyDk1daxfuJ++0Kv4wG2kUVZS+b2tuJj4OnpjA+aUjZAse+7TG+HeWDeEkMNvv3DrzBtZrYTmDtb/cJ6HCijqK3SSiMlQXDQG5fLK5eLYZ4iU5xYloXWK6Vtj8udRZlCqQfGGI6ScM7hg+f9+s5da87TEx5Pzpk64OnlFf164WrvHPpg8hPH3lC9sZwWns6vtJ45n4Tq653BqCGGjl6pVQsnXsG2rtzfV15enik144NlCgvBTfz6x8+8/fILy8njXae1QbAGbxWlZZlG24jRhlw21rzx9nnFOU+YFNM047whl8pxZEoqnJ8/EJ2DDrlUtn0XXbn6CZQrAFp3fDCksmG0YTZPfLz8giNdaeoQE8DsqLlxXib8Ku5tZY0A4ytsq6CSjdbkUmUKaQzBB1odTFHqwKE6zoLRnpobumh8sNzSG2V01rdC2As1d/b9hnUGG0+0bglhYZ483Rd++d2fsG6rGB0MeK/QRqGGZppO9GpQ8wAKtXUGDXxnuEHaO/drJ4YFpQbeeS5LoJV3kh1Yayi18fryxBQ919sbfXRinHi6XMS2Fwz7kZi8wzrDNDlKgV4N1gyeni5Yo5isJxRHR5N2MZo8nRe8ls4PyP9zy5XTeSblSvDCJ2y5cZQd5aQ0sdYzBU80ovn/8FXAGvPbgVE3K2nfMaFiQ6HUxPl0YooT+Ui0VjFWowz8+OP3hHAGPQinSDwe3JzSSXuiI8zzeY7c8kY7Cm0Yai8c7QCDsBf7T0Kg1VnXVWpaDaUU3vZGOiqzn/j+y/d0VZhPlufLk9jS6oodHj00znhJjdAC7FxvAujUSmONxhih4mqlaa1SuyxwZQz9wbg21tCUJuAYD1IrSjHNTzIBpePjmW+++pv88OOfkFLn4zcfAYHSG2PJuaBNwRnDsd9+646flvlxSe2cTx/58vkzThuen78ibXf220Eumems+ObjK7k1civk1LhvX3i7dhiaH9Yb6cj44HDecCyW3jKqG54vE7kWaq5E77Fas0yW0hp7u1FbJ2+D3oZc8M8RNwlYvxwZiufHTz9wfgk8Py90OlveqGUIPs95JmfpXZFKobUuX1sXuF1lELecAqNWULBMkSkaJEJKfLdDaY5cqXsl14OSC1McoA0vTyeCCgQ98+X+hc9vV8JhWE4zvSlu142dxrzs2KhpFLQyGCsyi9/1/F4schn1njnyig+Wr16+FWNEaszxhI7isO9jpw1xynvr0VbA+9f3FbTGWsNpudBawhuFiwGFlAL7vjGaoo9OKhmjJ7756hv0NFi3lVGzRKsMRe2F2jNHLrSuCcHhbWS9f+GXtXG9faH1HesVU4zMS6SUglMTrW0YG1AMXl6fqG2wzAvbtrHvK1/efskYhnXPWC2nSrMdbSNHUaR6UFvBOCPyYRt4f3tn3XZyqixLZD4F0pEIfsLZM8ft4P7WOHLmnjZM0PL3zhvKKHKt1Cow0OPYiWHiugaOegiOr1tqUVKGdcunLzcuTxcUE9aJFijtFW062y2LFmiZid4RnkWaW5JhrQXveUwp4TgKSklNPuh4GzgtT1xvK8eaCGEhRJk3uDYxP1/47lffkXPFWU9OB4odbTSXpzO9arAddGdU2I+d59ML6qdgfwMIYWLQMFYGIUZbsI3P23fU1ji7J1ox7NuGdUp2oHKI8+cRVTL5yN/66l+hlsaX409wUcy9qezk/KNMCLVi8oYYAqleKS1jLBjtUEZJLzZGrDN0pDc+BQi+gVPUcgUq+TgweiYlYZ475+Q0aIPgI7Vl9n2ntcp6H4Bhns+03FjXFdUV4XSiK3ENPS0f2DaZUPbWeL+9EcLE6/NXPD9pPn5U8sPrrOQAMaGVIh0JnOHjz/+A++2NWHbWvHK73tnWho+W0/lEcDL1vDxfOPY758VxfNpQGJ5fL0xxovWKc4HPnz9zve7M08I0WXI+KDUxWsF5x/l84bREBh0XLKcnS8mQj0btHaUNn7+8UXPHacegY6PBAMYIJuPl5ULOhU8/fI9xA2cvqBFRYQjUNCwY90xrmVaA7liPlaEzOkGYJqbJ4zwY+1MQaGmFdx7nnkVbomDYQWuZrht9KLaS0Sisn/FOUerGMNIrNViMCZjuSNc7LQ+m8MoPP/6SOnYwoK0hukiMkU5mqM5xJFJeoRvCcmHdbvQO83LBWI82J9TQ6EeAVsmF/dj46sO3tKczx7ExdCE+dOV7vpPTQZuEvXKKL+z7HecDpUArjSMnmQoazW17I0weazVadV6fL6zrjTgL18SaIPLhbsl5wzlDHxUFOG3Zj4Nt39jWnf99vxOdqDi7bjxfzny4fMB5x+gw+8j7fsPFhVLfYGR8nLEamSoqKBWUrnz9sw/U3PDWY/UgupnWIrkeMmSyivfbZ97erzxdngXeTycNuROlphgatNMYp1nXxKdfr8y+sO+JPe2cLws/+9nXhBhZ1xvGGkFDewvqNwFfBW0MOTfyfmC1474lwqxQyGW4kCXY63c8vx+LHEUuIjmNfsJ7z7ZvvN3uHPuO8zPOibT29fzEkVfm8Iyq8OP1CwPweD48fc09vzO5E+/XN371+UdsEDJW7QdP5xNzOTBKob1Dj4jXA+MtWhtCmLD2wQ1sil4MOSdJl1OOS3jFXRaMgtYbfgp4G3HWs6UVrxxaG/pQmGGwwxH0ROmZaT6LMcBUEl3620tk2zdqlqiSj88/477t+OiYp0U6CylhtCH4mZJ24YargbODECPGaZ5fz7QqRFzTodeC0nBshU+//iyQJCyf7u8op/n2m4/c71fiOTCdvBB5e38gkTsprRgtzPjemmTzhMgSn8il0nrBuxOns2UMxX5kck14H9DK8vl6I6dG8I4wOVy0hKKY5hllNJfnE9ooaisMpYjzWRDYNaGVIrcs3welsNZxfnKYV085GlOKwtyhY6y0Fd1PoYWolUErcQH10bmuNyGuYtE6EIIlBE9KO9ftnRA8r/EDsVpez698Sp/58uWNrd342fPP+ONf/kOOmsFVrHNoE5jNwmBQGxSVYRWjxHHcCTHydH5CK9Gk9z4eYVqVbcvidD9butlQTeFMxE2BrW6c5mf6yMThsG6iPfAMxyFBVSiNMR6vLWs62O6Jmhth0mjtmKaJHgatVL6s36MMfP/9j8yLY5lPBBeAwYflzN1YVFKMXtFOIkXmMFNKIkyO1g+GESlyzZBywvmId4497fzhH33LkTO1VF5ffsbQSeSzTdDXzln2dWcMgzaaaoSBo7WBLhp2qxQjGY59QynITTKHSul4Jz7dlBpfPl/5+OFVMpFi4Ol1wlvPfLHEaEl74b6u9DEwxlF7xRjNslzwTfRIWmuMcRjrqDWjfcNqxfVtR6G53xOzm5n8T0C7otTAMKg5YZxFKaTeBpZTQKnKGJrn8yvQySnzZ+uvaF8G+175xd96YTkFjIX7cWU4A1pxtmcmN4EZtNFxNtDKhlUTrUPaD4x1DAatVdFbeBEg0QTC+fS0CGtRwVF3op1pLQlOoSvImu9+9T02auIcOI6N4COtd6ydMMbx9fIiBq7Uuczw4/6JnBX2UGikIzRPM8ZqDruhtPw51aHXTGmFX9UD2uDjy0d+fP+eY5csouUcYMA9JWorKIRvEqaAf9LQYd82lpcZ9MAAeojlLliLsZLl6R3UWjgtTzJUquWBZCvUJhwZ6zy5VvZUGBasCVDcgzwAOVVC9CyzxZlJskubDNZSXsn1IIZI2xu9dnwIEohmND7M9FY5ykof+sEcH/SROdJGDBHvhY0ew9f0ptjWG2Yo1F+1T66U+vvAvwV8P8b4lx+vvQL/NfBHwD8G/s4Y44uSd/tPgH8T2IB/d4zxv/xl79F6Rwe5ULWeMQaeXp5E15wzxni0tmzHLvpwJTD5foYyOv/oj79nehmcF0U8nbm8LOQiO67qmi3d6aNTesdrx9PyQu4FP1nQcF/fSeUApWljYIdEGiqlCLMglfNxEP1EcB5UozXwxnDLXxgO1n2n9Cq0Kh0fCQyN2jLffdlpFMFI5EwthWmaUR3U0Nw/XYkfHR++eeUTwt++XVeJZUxXSh04bzmdTmz3jWA8g4G5TPhg6GXQmxBje1PckuCnW6vMk5Nc0iDMSdWg1MwyS+Th6/MH9u0TtWS6klbctIgkwT26V055UkkMLVlLPgRKbuR8oLFMcSI8hFveG3IpONOwVtGGjPnpWu4jTTEeG5WzDmsFTx2swbgOyuCU/21bMJdMrw2/eHIpkuhnPVtJzKeJ3grHsf3VFjnwnwP/KfAP/txrfxf4H8YYf08p9Xcfv/+PgH8D+Bce//yrwH/GX5LGDI+LZ/CyQw5JAEbJcCiE5ZHGK95PbTSlFglr6nD6oCmlgSqknNHbFyFMPRKBW0tYrRgq0ErFWMftdsVMhjgHSkmcZmGhdwfOLLLDDGm/aa1RGoyWXbdR6K1wv29cllcu84nJRwqFdb/LqLtUQo34OVD8gbKD7UiUtqLwGO3ZbonvvvslzgWUka6FtYpCEYf7WXJ93uoNrYQk8Ha78iuricHhJ8MUJZGtUwiTJc4nEbIZ6U/v+4ZCo1Tg+r6xbbL7v7ws7Med3mHbVqYgO6SPHpRCW8NxFAyIsUNpjA2PQY6l98oYFe8toxtCCNRa6B2OLbEd+ZHirDHWkVJhDI3W9hE07Fim5SHEa5Sy41xDW9nB366f0crifWD0jnOOPR0cSfyh1hRu94MY7SPK8q9omhhj/E9KqT/6p17+28C/9vj1fwH8j49F/reBfzDkrPmflVLPSqlvxxjf/WXvo7V6JEB0AXH2gTH2sfvtBBN5nj5grOXz7UfRGA8ATYgWiJR0cKTKYOBcJCyR9bYyekUpTYwTxkuimDKirfDe42YZKmksae2kvBMnJ0xDrSg1o7XGaksqB0pb5tOJI21cv7yzrhUbLX4WUGbuiff9xtgrT88Rqxxad6xx6Gow6sbPPzpub2dcWLinT3z/+TN5FJ6fz5wf4+7WC5enhXVN3O4rCk0MAaM12nZCCOJqHzBqQyuEK5mzSCHGIARPrYUwOZbTDLxgjOI47tKK7A30oA9pH97XDVLFGsP9dhfmewfrLK3VR2S6LMbjEOO1dQ1tIMySChfmIJNVNMea0dZxXmb6qPQxHkG1EEOg6iaShqaprWG9ZZpmiWPXCqcsrQoAVjYzxe3+hd6E0Ot9YJ7/evrk3/y5hfsr4JvHr38B/JM/93F/+njt/7HI/3zs+NNLlPj1DsZYuVkPxX7scvEwnjE0OR+MPGTHq3JRUUrDUBjrZbeVs5HWBl++vBNDZJpE7+D0RO+Vo+2oLvHlrVTM0DgrFzwdB5PR1JrZ9p0jJVEVOsfoElWitXQG0Bp3Dpzjie9++T3jWrhcZubo+aOf/Zzt2FnbndvbDa0loOooO2ut/G//uKLcRO2JTsUET+2Z6/1GzpnzaaGNhwlBK87nBfuw8h1pwzsZdFlt0cYIi6bIEKnRxbFUFGva+er5A84Z9iZ5oKMLrLS2JGzIMPF+/8QYFmdFkWi1w8eZXA6skRNR/cYQcQoss6E3zbZnrDW0Jru7s3Kn8d5Irige7yOtFo7U6AVyVQwj0lxrnZwErZLLQWSW5GVnaa1xlENcXkr8qKfTiWkqHIdkBe3bKoK+v4ZF/ttnjDGUUr/7Xf7Zf+63sePf/sFlHEfDmolWK7U2Wi2gh+TXa4uyhjoaKR0PsunyuDAWlBITbR/14ekUl42xEsZqg0N1hRsWaybIjVILWjvGEByGGQHbDVZX0liFYDSkVbgeB/M0P5RwnZzKY6QtMSjGwDc/fwaGoN5q57rf2faDoUDjOJ9e0WZgdMLZmWPPXOYn1rTRRvztjmqNQXfF9Xrj89tOnGfmWQs5yjty3jFmSAJb10xIJ6f3RumSV1rawfPzSdSDw3KeFozTtJLY8y7a79Lx2qK64v3tnes1UesBevD8eqbWHTM8y3wi14NeCqp25mlBjYHC4K1jBBnEaOQk7q3RyiCXBEbaviU12ujM80K6i0nCDEWcIrftBrbKbl6h2wFWSrWUCterYLOnaHl6ueBsQJuGtRqjPSkn+l9T+tuvf1OGKKW+Bb5/vP5nwN/4cx/3B4/Xfufzm6OvlU6MgdYLWg+cmSg1UwGlJZFZaZkqMvojRyYTY8AaC15u2gotMKHaHyGw0hrMJJxdyLkR/EywC8e4s+Wd/fhCOwrKNrruMtFUlvNy4cPrR7xzWG059o1mLLklnLEYa9n2DR801lgpSbTBmkAsE/t9lxSLAaMNonXYWHl9foEEalSq1pQhXO5gZcBVe8MG6VzUUnFD8RTOfO4Hiw6g4LjuvH+58+Vtw58N3/zilfdrJq2G/+sfvWN15/V1Avsroo5opxlpcL8fTMExLNy3d7ZdqARaWbyz7O9JyiKvRAB1QJwm9nbj7XbD50AbCe88WskU2RpNreVx+sjO2wfkrTFNlv24w1Bcnl54enKUnEh1xU1N8j2LIVwmUEpOBOfQehCmC6MPnuaTLFgbGCiKzpI40QrK/u5l/P91kf93wL8D/L3Hv//bP/f6f6iU+q+QC+f7/5t63BrDMgVGkC9YqYNOQXZGya/fto3gPSU3tNb0VtAaQpCktBAsdSu0BqVn0S5bjx6BXgaDSqcx9EE3g0KlHTfBSNhIPJ3gMshlxRiLeZIQ19wKQ4tD3T98nd4EbBFzQ22VZT4L3bUeaAbHvmNMZb3dKblRcuXlqyfRZOMxwFESue400zld4iMIKpCOjVoLznueX+UHplbx0e37gRmaGAdrgj4Mb7d3vuxXFuf45/0L7nmG0pntRJws86I4XSRlrfdGzSslNZyG6BTL4pknh3cBY2cxiTbF4ha+3D/z45c3fvz0xvlsCfFBVjARaydKLeQtY4yhVGHLTNOE0g2jQaPJeyVlR2+Wbc+M/M7PP/4Bn/uNt+s7wUe8DSgSmkLKEtxrrMMxyKWgrRDMamlYEwnxhAL2/Uo6Cpfz8ldb5Eqp/xK5ZH5USv0p8B8/Fvd/o5T694E/Bv7O48P/e6R9+H8gLcR/7y/7/PIMxthxfqK1jDYNrRRWD0YHtKYp6Zta81ATdlH+9Z6w2kmNrKrs3KNirEKrTm+V9f1gPjs+fvw5R3rHG9FjDBogWTgpyWRzDMORpCOjtVCmDONhgWuknB8yBGl5osEaj1YdusFZ93CqDE6XSaLEW0fpKmnSuksb0lqS76TSqCXjjaeUg5QytRbpT9eGj5OUPAYSipx3Ulas+4FWkctXE+6imaaJ26GBwlffLHgv84bWgd7JWRAa33z7xBgLvWbo4p8cfVDqRh2D48i0UrlpkTC/fFjAFXJa2VJjucx0Et7O3G7loXGJaGPw3lNro9OwzqEwTLNn3TZKEaz0rVz50x/+Mc4Hns6nx8RyYk1XvHOYh6uqZMk5stqTS5LyEsW2N7Yt01VD6Urtibfb+19tkY8xI3eRKgAAIABJREFU/u2/4D/96/+Mjx3Af/CXfc5/+um9Yp0Cldj2d3oT3HJT0FvDxchp9igUzYCP0rKqrVCORKFyXO/4GIjeo0wEpdFKY7Th+fWJ3gulXhmjiJvEe5Z4xqrAUa4MNK11Ut6k46K9+DdrYWiDmyZQckHtSPkkLBHLnjZCDAS/0JsctcY4gpu43z/Ry4HWhpwLb/crWimmWSiw3kbgYPKe+74TpgnXPHRQpuK8lZYowhF3fpH3NSIXNq7y7CemacZqj1WG1hOoTq2SxfN2fac3zTwHcpKI8ClG5jhj3ETtlZEPGg/OOpotbwLStIWnp4gaTgIQekNbRSmFp+cLCoMzAfSgtQbGoZQh54PoLsKKNI5pmqk1SWfFQK6J/LgfxNAx2oj3NnX0aEyL57bdKEnCDYwWAFGrsCyG8xJJaWPwiL78Hc/vxcRT6jCZuKUkFNpjdZxOimU5M4WA1YYjH2gatCR0K2DyHu0snf7bo319X+lIdr2zhtp34hxwOjC5idQ3XuYP1LKTyyYdGtUxqhGjYZkiqmm2dcUbjWIwSqUpQxcnpYTnKoV1nns96LWhjKXnTtWV0TU0cez4ETA+ELwi+BljDQB9dBgNqwIxnNEmUEejtILqBtsKtWWMdqAcjC4sE615eX6W3c3E34b85toYXRPsTM67XOR6Y15meMgVnIt4N9NrJR0V3XaUaZKIpy3m5YRR0nWSgY0nuMhxJAnb9dLJKSkTvWWOUVAYSuNDeMQdrnh7Yk9iFrfKQGuSYbpfGQhddz8K3geM6SgTOO4VPSK1DY6j0bJCa88cJ0qG+ypR7vt6w7uOdzOtZ/Yj/c7l9XuxyEcf3N830IbT/MwpfMAoxZa+oEfn2A7iLIgFYydS3hm90rqUEEFL4plzntYq3iLslqroOUsfuGT2ttK9paO4338k7cdvv/FdFUptIvF1nW39Qh+D8Shbeuuc40du6QB10GpBK08fg2ikh3zUhrIC149e02qCLkAcbUQ64JcAGEaXmHGRDAxKlSjtVlackd27D7DKYh9lkzUGhiKGGdTAeot5QH/EFlcoPbMlMRRI49wwhxNtSLmkBoxRsT4Sw0ztmT4yp8Vz2zZCiPTSWPeDnCtbzbxcPM5OjNqllaoU0UdUN7QCwwysN3w8/RF97LyrXwnGo1doBmMDY3RSPuRCa2SyHOIrDE3vnVIGKUFPBWelbHHOEaKcsCFGfha/IecdFxXzErjfMuu2IxeJv/j5vVjkgPgGQWStughbr2bebndS7nz1zUdG34lxZpknQhf88r4ffPn0Gesdu1m5LGdeLhPKGqKdeb++42NE68EYAVAc6aD2irIGj0EhCLNWMtpbjmPHaMcc5kfEtSGYQNARpS5sdYCWETm9YI1hXVdqb+RxwMi8PH/F7M+4aB7xKgdG94chGhRS01tj6F2hnZH2ocnIlc0TvXvoPwrKGMxQEnTrTxxV+unPpxO1i2bFuIRGPp/Rhtt2xwDPl6/Yjxs5V/FU5oRzchexyrHuG9e7RIfTRAb89PKMqOCNlHQ5M4cTg8rb+zu9V4x2wnDpg7Qrftz+WIRUsZNSYpqiuPCHAmVJSXJC22j0Lsi+WoQaEPzCy4eF6ILQcg3EGIRK3Bo5N85xphT/2OQEMX0+nx8M+b/4+b1Y5MYYzueZVjvzPDNPkbxlLpczLx+E8wEKq58oKZH2FWMgesuynLlcPHQj/Vel8Vai8Grp1CTJFNp0YAiabABjcDmdUX1gjWfdVq5bJa2V97Hy1c+e2d4/E5xn2EE2nr1vmMeiVF1sZt5FOg1YRKZaLNoOTDPsxx2T4OnplS1llDGMVrDOooagFby2WB+pVbAL3nhqTWhVMBScW8gqY7SmlhWlTzDEYqesQw25jCulcCrhTcQojdYeY2QqOmpiCY7THKlNsZwWjJJL6nbcmOYZ7yKjN0rNdKXQiARaDUUph8CRBhitWeaFoySmKF2hPjrORNohd1kfI8Z7xlDUkaXNqAbW6EfSX5MEC+1xZsaZLrHy0+O0jmJnVHT5eiugFva6SgnqHKAI3kr5+NfUJ///+Rl4pxjWEYPoJ4y1cqsuu2gxdCfE+THUaFijqCWJ7cx6QTdbhzWWmjKtJkoBw4TFskwLW94EbxwWvAv0kaXvqgrTSfONubBuCa1mepIA2NYSU3zhux/+lJfTM5MP3K4b65o5XTJPz3IsT7PD6MCyRJx1HGtmU4k5RNANZxxjdLmQ6kmESkomiEYFctvYjzuDQe8FpQ9x1BgHfdBawtmJWg/KUVlvG/N8wQRFtBOpbKjWQVdKa2hTUKY9FIRDpKzd0YZmzztPZ5ksLjbQ2ngI3wytK0oTYJJ77NI0S62V9VjlIszAZMfT+UXYKy2jFNS8klNF2YGfAjkXWm+gtfAKjZQmwT3wbipgtaW0BB20VQ/ui0ejf5tV2lqhFDHQoCWusrXH3Qihhv2u5/dikY8BrYFVRnay3kB1nJsYqtH6Ia/nRCnyBT1yJUwTS7igh2I/3sUF0yrBe8md1IOqK8pD6XdiNCjnULqjTEP1TiOTcqYPWVCXDw7z4Ox5d6K2SrQnns5/k5Yr1oF2Z5alMy1CcOocaCsT1lYrR2lc8/vD0jc4UhJNjjL0WtnrBqWjjaUoWHvG+4ngJowLXK+fUVpyL0vZsSaI/9VNBBfpo3H4nev6jtrABUVKO8poPj7JSVXLThugjGJ0ufe0PjDWsMSF0aHm49F6hTYaKBliGSO4ZgEPeYx37EdDuyCsk6F4dhdO4UlOr7aRyo73huYd53kCo+S0GBFnJpzxHObGoDHGYJ6MbPvDEFSka0HutSZMx14r2hqBCbVBcF50+fY3Q0ExVKdaBFj6O57fi0VurWFeJnTX7OkuTvqhH19ki+oBg6aPzhQvbNv1Qc3SlJbIaWPLO1OItJYpvdGphLBwjoFWm9TkiN5Z6S6tMGMoJT8S3JR0L2qijE70kS/vq4D924rGSn9ba5azY15gdCWfazg6VTB0rWO05enygSMneIy7S87MYSaGE7L6xCiB1eS60tuOsZGUBZ38/PLEsd/Fa9orajjSfjC6Zlkmni4nSZ5QmqPcic7g4xndFKlkePwdcy30Kuho7zxpz2wpczl5nBtyIUX4iF095LhGlJStVey80EZD6UavmdoLYB4RJonSdlF6Go1yjvd0R3PGBUPPh5xCZWd2F/CDI3+h9Y5RIpSzVgJ6GR1tnCA9lEZbCe8NHi4njVGe0gvHcQfl5AdWJ3yYUeefgMezt8b1eqPkLJHS8UxLBZQh4hmqs+eGs52SBsvpGas1ymiutzeZkvmI9YGaG60bbseNaQxG76ScMVazLCconRgdNkyMKrw9rQ21NHLKrMddSFBRg3JMQWru+/2K94E2lMQukvFhZtAo9WCogX188Z2NdDXINYn5AKipct3ufEpvtCIG6d/IWuNkmXzAD431F7TO3N5v7MfGfuzEGKXToB21wGn6yHb/hFUDPyucsWAUwQZyuWNsRxmNkO811+3O/Xagxh1nXvDzmZzvD6UioAa9DnLeCRH0EK74sDODQUqCj/vy9kacPH0UVJfFKOP7IshnIy3cH35445u/ceFyWSTAtgteeRQo2y6cywbOewnc0h2FkZSLXrFK4YzHBS93DRNJtfB2+0LrDaUyZmh+8aq4psHefgIXzwFMkwAfodFrfThcFqwxvN0/sW47vTaCnfj6Y6QbaKpTSiMdmZFk5E0XmL0ejt6UTN38wrrfWcfOYQdaXfBddlI/WZy3gpxbFpb6RK8Zra0IuPoQLYY1VDJqdHI3QOF2/yXGIpwWhtT6PqKA3iQho6ZCTYngAn6ObDpzDLF9DSzTPHO6eKbwzOX8NVprQqiUfmdqlnxIrpHSitv7itaOWuF+O7Bu8Dpf8H6SrkrLlJHRRgEaay1aw9cfv+KydGopRH9CW0sfkegWsa/VHYUjq4M9vaOLxHsrbdBY8aCOwTKdEJd5ZfTOEj5Qe2GMzrZeWa+Z+pDDjipDHaMU8yKCL60qU1hoFugdY0RpqGSgLLW4jsxqRnVNqonSN0zXeD1xnp7YjhVdAsHP/PrtB1CD1Pbfub5+Lxa5EqmN6FTUgC4dCZQhp41/7g//JT6//ymfP19RdcKrJ77/4c8oKmGtaKytM8TghSPeG7EGrFUYLd/w6ANDdcxjHF+LuLyv15VBxTmBDclko6OQnakPaFVTWqVTGKNxXd8EdB9mUI11v6OUZV0/cZ5P0r0xjmgntJ0ZQere1O5cXgLnp1lEaOrxnqpw9O853n4NdOwIGGPx1nK0wi3dsdbifJBY7tt3nJ6CvPe2sgyh0A4e0B/nAYXRQXTwJmJorKNyX9/Y042n56dHhIpGGyvtQP9EyUnS7LRBa6nljRYQaalihmhd8HetJ0azbPcDox0pbaR8MJTl+uVg9MayTNAqqsNonWAjNk70ttNGp4rfhd4HVhtyPTjUQPXBUQ6Cc/zw/a859orShvPLM5+/e+O0FObXE/fyhWn5CXBXeu/0IlM0PzumKYLulAcK7LZ/hw2K05Nh9hPOdMZNhj+6G6KPxMmT6yopxV3Akb+R4E7TCf34Juy7tAG991jryKWT8yCVN1zQnE/nRy/5TvQT6cjse+W0nBl1UKnc9433253z6cw8C1LCmYAzYmiALtCglnB+RplIqTuqG0YTeNEyPbNuN9Kj7emiwruJWitHGvS+U6vgnEczGCctOWM9vVX5IaVTSuK2vnOaLlInD8e+ZrRW+CA7+RiZ3go+OGI4o96NmIdbwrkZMJRysB9XnPMwDJqA1Y41/fDo8kScVxhjKUWQEKlc8eaZ8/mCIjG+kSAx7ydKGuR9EKPY3vb9ilYyBNNKoRUiJ+giW9ZW+JRKGQSeroWfvm6kI5Nz5w/+8OccNXP+ZiZORpoSZVB/CuVKH4PrfsMogxmwp0YqEHxgjMq+FXwwGCW7S207zy8iRy2loyMoW3HKiZMnZxE1KRgqkGohuMA0XWjrlWlyxCk8rGGKOXj2dMcGIxqLUvFOUVunDfCzRzlwNhD0BadPbPvKdi/c3j9zPmusSzKls0/kmoQNaBy2axigdMAZjdFg+gWrJp5ipFvN5BbstPJ5u2JtxM+GI2/44DHGECdDcBPHcdD6ISWSkkjIrsS3GmLAGUcblZzhOHYGkNI71jhCEN+m1TDPUYjBVmF1YN/uaO04zz8T5J4S0NJApr3WRtyjTTnGQKlBLQlrG6VpEY/lDWMbr1+dIVuOltjudw6jMNFSe/vt9zO3Az3E66lUl7gZF9iyBBRPPoAypL3SkmQ2XZ5eMM7glcNGT4iK1gbaLHL6/47n92KRM6TNFm3kn/zJ9zireX6d2O2Kcw7FYLt2UhrUunKKJ6zrDF2xVgYHNYPS8Hb/jFIiHZWRuSFMQViJSvMcn3H+EbNXAdUfgibJGGulCx9FiyqwVUUfDTU6t9vG3/j5z9lvv6RnxRQ803ICKi5o4bPYCA1K3vB2YvavHOWN2jasj4yesC4wlJg8/LKQ+859yzjnKLmTjg1UxsfpwaTpHGUlt0wp7ZGusOOcZwqTRCmGmZQOUJVpDsQoQimt1aNTNbAhUMqGDAgj230leIl4wSIRMEBXB2NoalsB9Qgm04wGqSRKldOn1oLRG7Ue7MdKSgfRzY+6u+JOhq3seBWw1rLdVoKXJL02xJciJ9ROb0OCgetgaweKQsk7P/zyyqiWjx+iZHjWxuwEOKpHoR71UZ79xc/vxSK31vLzb36BKoGc/1jyf+IkzO9pwijNkQ6cV3LktY7WHUNgvSVuZRcPIZUjD6xXkIVzboOn1YJ1nmEG+Shsd8Wx3xkWvHWUIj7DfqycTiessfRRsVpjJkvOnfubcLi//+HP0KahPHQKHREwpS1Ra8bZIdRc79FmAE3G+lQaB0N1ek9or+mqs9UbRhtMkJ3TOEPQntbFAHIcB+movwX/jDE4duE0jqGp9cblaabjuG9vWGNpfaVVhfcW4yLrUTi2lZdX+UHEKLTtuMk/wEkapQ0pPT4vjVwyMTpA01sljbuUlQyU1lzf77SqcWbCWIV3y4P97lEY0pFwYX7YDBXrekMphXmYQlopqDGINqJGZ5me0VrzVj7B6FzXjbcv7ygf+fT+Dt81fvbzF5x1KCWZQtYGkeIeP4E4FZT0ROk73377QbTbfQiAMleG1mLqvTwLk2+oxxfY8M38Leu6ga4il+3Qs8L6iVp2sNKTXrc7YQ4Mpdnuneu10O3Bhw8fH2YGxVAO4zxKC4XK+4CximWJnKJmKEVXmX3v9GGYvOd8eqIeGeUN9+NgiV/zdP7AW/o/6a3z+fqZVHdC8GityfXA2Umc52j2tKOG7JbGeNQjj97ZIH7K2RNDw7lISoXjuGFMZLzfQCsuLxMxiCz4dF5otbO/F7a18Poh8P5259OnG8syU2tHj4JVVhSMzpDqTi0J7yfWdcN5z2jSu84I6zwdwj3P6UBbjTee8+WFVnjkJSlqTaR8UFNmms44G8kFWjPcbztp35iniXV753zpeKMZA4504L1ESjrnccpwmi3X952X12ch3n59JuXMek98+9HhQyKNRq5C9lL2J1CTDwZKFwqd0STTxxhJINhudxQePxk+lV+Ts9zkRR89US0syzPr8YXySCU4yop3BhvAOnj7tBLDE++fN5bLgjKdy+uZ1j1KdRQyapbWdaf1ImFQ6YYuCqUsYFCP2MLTPKPRDyG/ftC2MibAl9sPbMfGWt4wBkIUU+6osJckvWUtrc1BJ5iJ1gdjKNZtp9aOdweX5YJRWk4UawQgetxoj/ycX/zBz6ltkMsXGdpYJwpC28Tl840jt5XpdOF0mR5oB0UtGmtn0r5RyoHxEnYAhjBFtNZM8SQWQ6PFWKIG1/tKV1BSor4fLMERvEhrjTY4t6CdYSCnTgwONwbHVsmlcbm84IwRvnitYgq3jnW/cdveOc2nR2kZaV3xs28njB3UOoj2wrrfAYXTjnW9oo1DIfJs+AkMg4yyWOXJapP+67yQc0Zrw/PzK2krHHvm/HxhL5mOYp5OLPEZbwOtdtE8xBk7MtNiMUozVGfozMvXF457x3tL74WUN+73VVw8xVLagQ+Gl5evaSUxaOyHWOm0AmPEC+m056uXn/N6+oZ/+Mf/Kz56lNEoOmXsxNmz9o1hFc+nC3oMIYLRGK3j7ERwwnpZbxk14PQ8EYOjNEeMC947RhsYJdQupWb6aFgViQGu+coPP3xBm5V9a2iKGIZnmUKepydCDOzpRko72kZql2ACb2c+PP+CY3+XFD3VefvxM8YbRoX39ysvT6+8lyvGa9wksFAfLD4EmuoMDNacWW+fqK0wLfGhIzFghmjF+8Ps4i3TYnHRYdQjKGsY0rETg0MrT+uDmne2baP1yvwIPrPec193mVP4gVHSzjUucnrWkuRsDaM3/hKo7e/HIpew2gp6MMcL+7pK5PYhhCZn/m/q3qXX0m2/z3rG/b3NOdetqvbe5/jYjo0gQSRCQoADCtDgC9BFSLTSoIVEjw+BRNcSTZrwAaCFRAMLYkWyiA1xEp9r7apda615eW/jTmMsW2mQQ6Jzgs6erVLtKlXtVWPNOd7///d7no7+dMRYjbqDXFO7S8qM0oJ5WSkoSg0IEdm3zL4FYtnadg44TU90Q49xBuMq798/EMLGYToghcSHjdXvpJSAN/uzgqF/bDZgVZBCs+5X5vlMEqHd50tT9Q1jg1jWmqmlMg6t5GCMeUsYgtVNub2smU4kwpb47vkZaxttN5VI9Bv6Dd8mKsSQqVJilEcIx8PdA9xVQqz4PqJkxfsd6yzrHNB9k/jmqIi7xnaq5dNRhDXx3fwJ1/WsYaFzPYe7O/YtEHyiRMX5eWVeVsajpQ8NADQeBnZ/QVmBlApjNQ/3IylHpNIIJ0k+IkSb61urWwE9N4pW349tfl4zNQtcZ4nF03ft+cN0Gl3fUNkl4dyAj75NtyKkfEbrvmXhRSaWSK6pUQ/mlVq/BwEtBGjZUzPcLhdKjdjugJAG258wQnG7PpNKRdqENgoq+LCw7x6UQdGiqgKNqInO9YgoKLX1PudlY/cB5yxKV6zpuV1nYgo406GEJifo7Mj19oqQCucMpXis1Sxniek71ltgiwkwXM6vPL07cToNnM8v3N/fcThMbalVUisOABlJDB4pNLu/kUrLSw9TxzD1b+9e5a+4g1W05F0thRATKVdew4wyBquHdmirRlmBknA3jeQoGIcjIey8frlRSkFp1TIltODVss74uJKLR6tm16sVpNJkMnfvT/S944N4hCJZ9yYO+PLygnND8xtJWHdP8JWcBc5pul6SQmTojw0eqlvOXCuL32ec6XBd21MM5kApgrR5aon0zlGKIO6F5bawbZGHu4laJTmEBphylULB2ROlFlQtxBgpJMZjR4jfg41nrZVlf6UW1fqRRlNqpO80kr3NYl3r8Z2vF4bhiLMaYwdyDm2poRsQSEnL6TQ1C3J2CAnbFtj3SI4JWQ0it/bO6eGAkRaJYt83UtixSvHh/Y+4za90g0EISd8NHLqBHCUvL6989c0Haqp8fn5GC8FkRzptUUoQw0JM7RsmpUzIO8aoxipRFp8DMRem8UhNEKJn8zPURiGQiKYlF5IqBaU0luD99I7n60eWeefu+IDflybCzZmcEvfHB4xuEyUpFEVUusGglSFH6EyHvVfE1GOMI+SA32b2bacWhY+RdWnvrjlXlHCILBFJ0tkjtlPIv4wHCxBV0FmN1orgd6zT5NoYODn5du0zQ+NW7jumazuMUjeU6nHWvS2F2paz5DdQqemIeXs75BK0JMWdUsGbG7EkhCqkGOhs003m9D2Yk5da2UMll43OWUpRDfUmCz7M5Fw5nabWHlIPbxzsNvpaloVaK04NTSblA/vmsb1GacG2ekpVQKYfHLIKQgxsXtD3DlEqfecwvWAqB4IP7P6C1LB7zzT1aCMRSpGT5wd3j1DbivqHw9uP98I8XxEdbwjknhgTVnU4e8SHCwiFlI5538lRUpdATol1W1AaTsd7skgIbSnV01lLToXjoa3oay083n9AKPOWLtwQRRN8wq+Sks8cpolhdNhOM9geoTPGGLQtaCehWIr3hLLi+g5rDhzuJnIuXC87fvUoJFIoYsmYTqBdh9Sq5bhC+6bTWqHG8le8w23e0XrA6op4IypE77nNrwz9RNxArhuFiNIGSqDUyNBPKNlIZm5yOGlY/UoVLTN+99C9QT5HQohs+/6GwjY4YbHakmImfR8OuQCMU6jc5PB+i+gaOZwUX5JCyiZ73dbI8dQ2ksFHOqtJ8Y2plxtLpXFU2qJiWxKub4EpqQTH44APnkGfMNK00aUprGnFB4+WPZlEFqlNF2g6dCWBopBKtEReTBhl4S2qqxCMxxPCgBQtt51TbTkQ4RDVcjc9EsuOEBplJLd1J8W2ZDkeDkjZ8fz8uSUve9vwGjn/VZlBiMI43rHuG1Jlnp4ekMIQfQUcKa4Net+phlcLnpL/8u7aE3Pm8t2NkFeGo6aGSM6BdWtbtMNpop8szrWyg8otlZgIdPrQAnPSEXdBzbk19JHtoX+XYBVZJpTSDN0RJVrwDQoxCF5ezmij2ddI343U0hAeFIghcFsXejdQRaHrO0pNrPtKFYIQPJqOse+5zjOdsVhtKEkwup41XH/p+fqNOOS1VrZtQUuHVBJnDSkLblGhtUBrxfPzK5++vTFOmnFyb6lFh9IObTRtX5fxYUVrg6waiaEkidQSHxZul0vjBeo2913mHaUFqAaxHAfDtq9Y12GtaBIAUfC7Z9sCQllCitSSCaFyPBxRopClwG8LtlgqESNVay8JDamQdsHDh3dc0kcOSpNSJYiGetNSICmIXBhtx3Vf2H1Gyb79OSVD2ZBCUrdKyDuIjAK0GlDKgNgQNhNLYb/Gpl0pFaQgbYkoPN+eXxGr4vGrIynNoBSpVrph4Dq3Md7D3QNZZEKJVAFoUEKxBU+MO53r0b0j+Z19byWO0+mRu7uHthgTbYehlKEQ2bbQohCm46sPH1qxmZYrul4ie905vy5UWbk7PZBTwtlW9AbR+OdVUIVGINpitirCvuPrSo4FYx3L+qujm/9/eW1bZHCaGHZspyhJ4HNhnI4IAcfxQPdbEyFGhEw8HN9zvb5QZSGk2O6JyuLUkZgWXK+QCpR0hNgY5+u6UKvl+jq3f2A74Gwhi0oREPzcUA0h01uNqIV+sG1W200si2+hKaNRGq63mRwTcW+9Tet8a/Ynj1aWbrIQBV/ffcU8f0c1kRCACr3VyJoZhvauJmqiHzqUa58YRmuMmni9nnk5v7KHghEdj/enpidXsrV5aCO4u+OB9+OJH79+QQiNVjRscoqUqpAFvn76Af/kZ/8Yfch0Q0UI02RkyqK143KZ23Y2C2LcqCQ2H7i8JlSCpw/xbYNceHf/oWXOydRaSDWw7jeU6DAyNS6Nj9RYqNZjbONG5iS4ns9vgtmC1m9XkgIpJQSKcgvEWggp4KyhcxPLunKajjg1sPhro6Z1HaAQ5Xuw1gdBbw8t9Zfm1vR2I2GP9AmW5UzX94yd4MGc6N3UHmaMpoj2BO7DRvQRkS37XrjOV2oVDP2bYdk5hunA1H3FfPuWNXgEkk8fX/E+Mx4kRWwcxwm/3XBiQCnB7bbg3hpHp8NIKR3Gaha/8unzK8krUnizvhnJ/cM9KW1Y6TBaowdN9Avee1ARgSHGtubXth0mqwYEHaUEBjMSQ2NxV9txN51wyvDyMqOVwkgNopFjt3jGh5UcM8sS+PHlM99dX7Bdh1ANnq+E4ePnnxND4batbDmgL5Z1qYwHi9UFhWFf1paGNAO2G1jlmZR2rFA4vSOlIayCnArLtpG3LyghGI8dfTfw6cszVUaOk4IxMg0H1hy4XK7sacUkwWF6wDrDMDbhb4g7IbZv1Mv5jNSGfV2x1rDuOzEYcWZ5AAAgAElEQVQWNB1fzq+s+87//ac/ofiCdZrD0HN/kkDF2e/BIa+1xS/9diOUgDTtDm17TWChqkosGWsst/3CLz595HR45DavdKPk9Xlh31dSKQz6HXF3YANSRoTYqCKz7QkQmKcDIW30veN6nhmnnnHUHA+OdV/49PFbDuNAUhE1WPru2MCWsiLVTswbukyMXc/TXfNjOtfhtMXvN7QQuGF6e8DdKVSUU6hiqFlCrjjdU0jU3MJYSUX0oSeGRBGZUjOpZDoDk9PUavnRD36A956UPLVUOmMZ+m94uX6LMT1KWLCS+1NBKEOnAj4rfBZIbbgbHNYZHvXEvu2sa0PHWWkJe8u7W9tRfKMn6Awlw/u7d9yPkVI1fg98+fLS4g+943Z7JZNQwnAYjxymsc3HY+S75+/QesB0llID67pR8oWSK0IIqs/kHCnVY3rJcDzg7NBaWcXTGY0NLVHqbEfOCmsSm/eI4ri9VrT0dFJz/e78S8/Xb8QhFwIELUthqkEpybbv9E7xdDB8lyQ+JE53D2i5Yx96vPdM04Hn18+EPaPUgNWlrcCVZxgMJbeHv1oCe1gxVvGLT39KzbR3Jl9RUtH3XXPoWM0333yDqY51f6WWSAkGKdro8rvXF5Z9RUrF1199INfINI50g8TqpiZXbw30NiNvuel5e0YojaJj7O4IPhA2zzSc6PoDxjUMBSWx54iyCmUktaz4LXK5JeI+431i7G0zPteVVBOH4a4tndSAUha1Zw6Hr3k5/1kb70XP3WmAkijFN6LA4LifuiYkU4aU97ZS1yPLfGbfXjn094RuxTmLEKkh76rg4XSHQDAMPUPXIbVsYKPouV4iTgzEXRJWRTLtmpJybCpK2fBxUgnC7t/+HaCJ3gI1SYSUzUJBQQpB8g2KlJ4jX31zT+ccVucWT7aan/z4F5g3Itk/6/UbcchLrlhl2mzcOrTuOc8XjJ4ayKZ2IHZer5/ZZ9/c9cMB5wzjMNK7ijaKu7t7KomYW4Z7mQM6KYZpZEwdSjpyjix+RYmMkBqUIURP53r6YUAKQw2F/vCePe5sa2JbLly3nXmu+NSAN2FRFFVgVMy3K65vBrKSZlKs9K4nxMC8zhipIEpC9ljlmW8X5vNOGjTHY4evK4JCzqXZk/eZpwfL6+vOzz0gFTEKKBJrC0YbtnBGas3L5UYmYrXmdHhHqjOXOTfQZ60oAf3QU2th9021Ut+465fLjM8e7RRGKyQXnHFUCku6sGwzRqsmABv0WwvL4OPG62XBqA5MZdluaGMgCzotsa4hRFIsaOn49PET797fs+8btjcYa9FVMQ1H1vXG7j2lVJzJ7OuCUEOTmZWKVG3x9/A0EENmnjdu18DYRWzn6A892n4PDrmQAmcty+7ZloVaN6bjPdaeiMmgyk4qkvPzjd0nxn7EJsi5cDrcU3nLSfQdiMi6RUopnE6ntz+gkqLGakfJmb8M9Gx7oh97FJIcCsFXhk6SZNuwWWvJoZFf76cJsQ+cl5nRGZ6mR9SYiXltfqJYEVpgjKXrLEZqYqyNxZiatsQ6w+IXsiocHye0kBSVqDK1rWMuSK3p9JEUCjXDYeoZR4sPOyVXHu6/Zl4+o41rtg1jUbXBQa/LK/sWKcwMXYeouUm21mesHjkMX/F8/imFiJEtvRh9pGaJVBa0J4uMEarx4NEIaei7gVQzqaz008ioDakmFAYfPcf7xnDR6m3GHgNDdEjZuOzG/ZA9LJQiMVoT99AIuHGns5Z19tjOvAW3YnMICcP5eiXFJhuTQlCLZBgOLBfPugRKNnz68swwuV96vn4jDrlSkpAzpWjG7gQlIYtgfW0VMz10MD0wDjPzujAdRnrTsYXAYTzw5fkzQhRuZUUI8xasUog39kcpEWsHUtiRoq2/hSz0fUWrvgE9bXpTnl8puRkjOjehJokzJ6IvnMaJ36rvUaZitaAUCdly3Wa6aWomCzFzPSus6tGqp9RAIhJjbEiIuKBNu5I5owhpIeeMlJokm2OzVkWqEtkrhIwEL9mWjZLhS/xEKq2AgbRIqfAxsFx3cmga9opHVMW+bTy9f6QG0eCb/iNSG4wasMIS08JxGqEIuqFj8xkjJSF4fLy11X1tniAlDalk9rSSQ2mQHxGQqtGx1vUGyDdOeUNYCynaBttWOmOamjxmUkyU0mwU1pr2jeEj1jm6bmDfFtYlEnzLrr/OV5zVUCXdw8DxweDXgpvgvZ2w3b8cCP+v9VVyA+0YI/H7ihaGFFbO55VYA8Ngmv8xBzqn0YJmaysby94+GrUqXOYb1k5QFLd9JiaPMY5UEof+jhBXtILD4ZEqElpLetexh1sD/7xBjbrOIqtA6oygUKqgHx0hLMha2OLGy3mjrJppGhn7EzkVYkzc3fcEqbCyRyjLeb8gteH+9ID6y/snoh2MMDcxgKjU2BYfVENYC5fyHQLNOPYcBotwGhngfF0YTw2QtPsFnyL7vhNTpeZCby27L6zzBesUl8uNwfU4PdH3R9bwQs4b2h4IORDihhCG6/Mzt9cFvwWmw4DrOgZ3IJfIuuyMvUPgWNcd7yNdp1AahGqNnuV2YzqMIBtb3IcNJByPJ863V5Zlb7ClKtnXBeccdw8HUGBHSYlQRKGkiNEDy7JhTOvv9t2IkQ5qQlTB6f5EHALO9NisQXwPtONSSSqNT6KtIYYdUWE8GbQBVOXzl2+xrkMpWiCntvTi9XbBaY3fCjlJsAUhNClEKpXrZedy21HvelzXtCr7Nv8VKXXJF3zc6N2EUhpIKGXpXGvD51rJZafQYVy7egx2wnSWfEx0pgcc+7ZQReW2WYZpePsoT3R9U8I41zEv32GNRVCJMRJzIdeM0orlFul6y23x6Nrx4fBvkM0X0G0rY5wDK9BDh5BN6Z1ERtWOw3hobZytoK2mcKDUiiAzOIsUsPuVeX0lC9/KzukGMqP1wG2+0vcTaVBoEZiGkeW20smM6yzL6lmSZ11nrDXkAhHBliK5FIzpGk7DBGIOQMa+4fGUlBwOHaVAya0Qcn8/td2FhnVfoTRcXueOVAElFZTW7PvK7bLjrMPvV4ahPQ+p4FFVYWXXtrrle3DIqZV12Vo9ShpqrcRQqEVB9eyhjQFrTcQYkU7xcp4JqfD08ESMO313QhrNsj9jlOV0GhHKcD7fGIaB+9OJsN+I+46wzTJHAp8DVSigtqkFzQta8ob3G5u/4bqBZCPWqlaTSwlrJ3KO+LRSUibVwsP0gZQXqhLsvpWptdZIDUKBdX2zq5VI53pC8S1ZKQ1W9UgBVicQmn3+zNQfMdayhNcmwqoCpwZqiez7ht8W7uxvURQIHXFG4f2GUlBlwbxxFJdlwVpLjIVljbhOYVXL67dxqsEYyf1jT2dOb/hrTdd3hLQz9BO9GzFatxBcsWzLjtM9Sgq0Kki5E/yGVJppGKkiY/sRQaUzI/puIOaE1G+zbdW1pGVudIMiNLd5RkrB4KZGFRDfMTiNtobDKVFJ1FzZt7ZH2ePGui6k/H045AicGzkdjuzbRlaVS3ilcx1CASogakWpxPFwbJOCLWBQpLSRc2Dbr6z7jusMMSR8yYwHxenUY7TBqYxzE0Y9sYbrW/dQo5Qh1sK2R5bFE0PlNDn8Hki5sK2F5+dnxiFibGkhL1HYfHsQpDZtuNGaGFqct6rEet0pogE4XadbwImGISYVdr9ThKcfj+z7inE9RhlsyQgtYRxRxbFdbrijRRTQyqClI0aJzAYzjuw3zxYC44PFaUtYd6bTRFWyPYekjOsbUbZzmtN4ROrmSg1hR3eGUgRbmEkhYe/uQVXc1Hq1QhTGw8jl8gpKYJwGAYfHR3LMiLeNqdAeWTUfv31GqYrSFWcd43BH2Hem4Y5cJbsPSNk0L85YxsEiZEVIw7LcsLoVsu1wR2d7jHPM241UEloqOjfQdSNUyW09Y20TNPyy1z+PM+i3aDbmDzTY1R/WWv/bX6d6PJfM+fWVWt7uw6bw+Hjfoqey0B0GRJX4GOhMy7fsXQMLCSVJWVNCRMiMlh3dcCDV1lRQSpPChlSKlAKvcWbfNqyR9MOEQGNNz6fzC+eXnfvjPS/bGURrEp0O9zw9GlIJTYPS3ze5VNyIIaKl4NTdU7NkuWwoI5FU+l4jhCTGnRICc/Ao41jnhbvpRCaAbJ8aMRfW/YJE0TlH2SsCTUgXjuOBZX+hlKY618phdJvgkCXqJDiYqUGQQmAcTsxrYyim2MrH3dCzLRuqtAfqkto0R1TxxohsWLaHd49Y0zWNorV0ThL2jZphGLuGnqMtc7TW0AlqEW803Eqg8tWHr6g5IKREoFvp2ldiuIJKOOfQtsNIgVNNCFwrpBDIKZNVwqdCXV4ppbCmVp4IPmIUrOWGrhVtTJsuCcv/x5j8n+udPAH/Va31j4UQB+DvCSH+Z+A/59emHhfsW8Ha0AJTtUDOb2YJiQ8Nxbx7iRyhhMQw9pwvr9wW394xxpH78R21CGrJ1JyJKZFz4Tgdub3uLPOOcT01WTIKkSvGQt0S94f39HplGBy3pQE+T8cDhYrpOpYtEqNHhfVt1Nfj6Bj7DqcHoo90o8PguC1nrvMXxnGg73o2X5FSoYTl/nSgM4bPzz9lmo6cn1/Z/UZMhWm8h+JJsbCvEazh/DIT49JgmKIwjUCn8OGFlBv51eaGs/NrxLgOHxMvl1cUgpgi7796h5KGfUloN3J3/x6kYL7NnM+fsJ1DiMJ2DWy0koiwld2/vMnHGkty3q4IAcY5om8VvVozUkas6hkG03qj6gilNpVkLxFGcTmf6bsOazX7fkM0HBG8WSukFGih3xpGTQYcQ2SPLQekZZPexrhjpWvv/lmRa6JTv+II8U1R+PHtxzchxJ/SLMu/NvW4UpIf/bV3GN2KuxTVEGqizZmz2KiyokxmCzNSSyQG1x8w7kiOkWG6Y7BwvlyJEYztSWXHryvmcUC6xKgmpNDcvl3puhbr3OKNGCOkiut7ukFTjX5jASYEoHTDqRkzUTLEUnC2oRESGViJKjUxVD5zGh6I4o4tLYQttHdg51CqIlQiFME33/wNLueP1CSZ3BGvCp1r+ZUaFJ2yRF+5vq4M44Hnj6/kVKlPPaFbKTrgTg4jZVv49A8crGD2C/fHdr8vWaDSTqmSHHdOj0dKClzWb0FIejvwdPeeKjQxr4iq27SlKPy6o5RD0b4ODfrTYVxTTm4hklNFa0kqiZx3nBkwasKHrSGtRSK+1Qnv7++4P5xAJFJs92qt2vWl2srxNDH1IyFGztcFqwoiBTqpEdmgpUKUDNUzuhNVVgoJ0YzCv9oh/6dfQojfAf5N4I/4NajH//JVayHlnWW54ePGNNxjtSNl/5ZyW/EhEmJBaccwHDBG0pkeqSSCgZQ25pzp+gmUx7kBYTTm7V5vhiaGUgq+vn/C2Z6pG4gxcttfQQScGzBYQomgGy6i1L2tmHVFCM3gjpRlRlWBoOGGY4ks/sq6LZymJ74sH8miIrXAmSNSOlJtRKsqJdt6JYvIXhbunu7JKTDaxkWv2SBcT4iNH2i0xW87D/dPpOh593SHKoptC3z5/C1PjxZjJCZ9A0rQy8ptviJzszZoJREFFJr5trU4qzFoo5nrmf2SEMrSDx13xztysYQt8Hq+MIwdSSS2dW2jWBI5VXIKDVRUA5svbGsTDbtuZBqm9rBNJofYBL7asO87t+VGLpGSgQK5VsZuRKmmX6lkYoxoKRl7hz6MSG349OMz67qjp9L45KkZ7HTXNrX7tv96DrkQYgL+B+C/rLVe29X7Lw/pv7h6XAjxd4G/C3C6s5yfd27XjT1unF3mdHcg18K2zKxz42IfTgN9D/t+5XAQrGWl6zqstiitmnPTCpSVpLJRKZiuyWutnSi1ENNKFpAJpOrYkyeUHWsKiNTy2zWiqqZo2iIl3jDiyBbO9C5jbMcyP7NuO9oOdH0jRH317geUlLluiTVsHKYje1jJZaUfBoy1+LAijWIN7dPpy/kTrnOELSORGGWRubCuK8YBRvEwHNmzZxhGOuc4f75QtMBhmZeCt4okn4lqJ9WI6/q2ys8FrceGjvNNySJMZssrKRSGYUKNmhgiuSrOy0dCWqEOiF5STQN+CmuJuaklr5cZYzVFZoQwbLcbp8MJKSUpF0ouzPsLiIw1HZJCLQGjFX6NFASltPyKMQ2Dt6wL1jq2bUYgsFZRRcUNlnnbcUeNyJWqC8ZZqkyQJDlHUo1I92tAUgghzNsB/+9rrf/j20//SurxWusfAn8I8OHroe4+Yd3I6iPznOnH9lcrpePh7j237RUpFVJ25JIpRTHPTZk9jgNKZbTuSAVCiE00WzO1ZpzVKNk17WD7H0Jrw+v8C1LJZApGWl4uF1Y/M9kJv3lOgybWQm9Hpu6eOieqiGSRqKIhzh7unwjphqgCA+ylFTI68TYLD5Hp2NTZ6zIjlWBdPd4HnBEIaYmxYqVDiYZMtr0gy4IQll5P3PbvkFJTxMSSdtRRo6pGLCPdoLCdQWnLumbSnqgyE+v+Vw+KqRS2sCGAXLa2du/v2deA6y39YSLHxPPzS0NiiEa7ui4B5xxZRPrp2H7fcWz9VHKD+3eSFCu1RlL25FwZhhGhoZRILpFaFbfLTggV10m0ETjXE0IgZkGVPfseEAi6rifmnddl4TrfQEl8LKQcyaFJuU7T/ZsFuhJ2D/JXvK68TUv+O+BPa63/zT/1n35t6nGlFMe7AQTcP56QSrV+oTSUnNiWjfdf/T4x+2b6kgVrBVLeEUJoQ2gp2UMkBs/1MtP1HSltKCl5//49KW+IqghbobhIySvX24UQU6NqvRUAvBeEecZ1BTtKfvZlp+Yrv/2jO27zFWM1x+mBbpC4DFKkZk0oUHLAaYs2Bu0NuSQO40Shcr1cG0V394SYGMaOzioKTao72g7z1lKqwIM7MaivyFtBdB4jHcF7Qsh0vWn31ONEKhu3eSHmRtbqXI+PHnKicwPXfWO+LXS2b1mQvLylE89M/QmxZcK+obSk744cx4l19w1klEH1hvvDB67LC9ppMoE13hqQKGuOdyPGNo3MuqxvM+uKDx6EIsf6hrjTJJ/Zb57j1IEpIBU5R6zrWkFalTZRK+otv9+hnebnnzesdoyHJ263F9ZtZbkltO7IpbD6+Vc75MC/B/xnwJ8IIf7+28/91/wa1eO1VtatVdFUauv1eBfZ37gjMVaqkE2AJTV7vPJ6vmBNT+c6QLDvrTzRYO4Ga3qC3zG6Kf0utxmnJkiVdQ8ILfj2F594fHxi6CaMqOwxU6N4y7ZL/mLPSO3oDweqKCAkOcOyrnjvcXbAp5e3uIFjDRXv01uxts2hx0NBKIXUklIDQ++wxqBNxdqeeb4xTiPIzOvlhW7oQEqen88YfSWEAsq3T4Tp0KY1iyeHHe83+qknxwZTMtKglaAWjetGlu1GDor74yNUj366p5YT2khu1xvd0OH6jtP9N7x7/zW9kfzkH/8ZtQoEEWEb9XfbX3m8f8eX87eEvGFdRwg7wUe2pJGyoqXB2oFtvpLSzjQ+sGxXQghYo7g7ndCnjuN0ZBwd63JpLStRiFWxxpVaEiE39oq2jj3Jxl3xlSpATIlpnKBXGHZqlsTcbM6/0iGvtf6vtK7x/9vr16Ie18JwsI8s/kI2hXWb6XdFCO0OprXkfH3hMLV3xZQ2tGy4iGXZWns9eLSxlJxJsRkop8OJGHf2XfDyZcF1G6fj142BTeHh8Rse70/4DT79/FuUjRRpsLajpsBxPHJZPnM8PvFy/rat+JNg3zZSioSQEbKlFUNoLrJULEIaxvrA6DzCzqx+QyvD7bZyDTdOh5EqNOfrz6kYFp+YS0QJwx792710pBAQJjE4xyUFlFVkCrkKqgVrRoQSaNt+jZSCxc8cpwe0lgxdz4u4cRgHXs8bNQWs6kgxMvSGDx/e8Xf+zn+KHQZi3hEEvv7B7/H3//h/4eXLShMpJ0owlFhQVZOTpGpB3xm0rI2CVTNb2Kg1o01uepp8I9dAlbk5kWrTNeoMNRnsUdPLynzzXF+XBpKqGimg63uKqPiQud12/O65P02s6xXneuZ5pnM9Y+fYUyGV/pefr3+Rw/gv6xVC4NgdMDqzhBnpGoIhlQIxoaSjMxPBZ4bJomTHYA/sITFfP77B6jXzdaUU2SD0BB7fH1nWGcSCdZZx7KkFbvPG6W6g6zWvl5l9K6RoqUXzer1xmBQPTwMlZ57uf8SyfKGzHYNzeN/EsMttZuhHIOF0Q15IOrbNsyxXzL0gl4grrSOqtQAEx+keWZrjk6LaSC7D+4ff5bJ+i/eew3Ai5b1tOGtl3s6EFJhv7e5qtMDpiRQqXa8aD7FsHKYHOueIMbFtCylXMAm0wfQG4RW96RBqZIuv/O1/6z/hw/R7XMI/IoTPSFlRWvC7v/83uaw/4Xp5wQpHb9udvcRC8omqJl6vz03FiEIZgVKFCoSwv63/c9MzigZPldZxvrVWvY9w/3AgkdlyRlqYugPzdcU5TVUJpxRaCAZzZLsVvF/YlsyNK1Ia+qOlACn71rz4Ja/fiEPuekPSM7nuTHc9Sh3w+8Z1abWmSCKnRNc1WtKxv3sLPRmkzHx494SWHbd5JUWDqpH7g2WYwOjKOJ443vWQBVpI9ru+jfMoyHFo0MjDSAgr7w4PWDUwfJ2hZqwZ4DKS34q2WkukNPzwd/6AnS+s/oVtnts7us5vzwoaoTaUFux+RUqNc4bD8T1WtfiADH1DRAsosXB5vtL3X3He/pzgL03EWtuc2cgJPSpeX3Zenq98880JRKSTAynMCAnLfmPeV+5OE/PyjM6WIjJrOLNsC7UKjIKiAVn5a7/zr/DuqwEffkbZKymeQaa2YFKOp/e/y/OXF2LZucS/QKF5ed5aRHZP5JpaGKsK9rDh94RShRAjp+OAcx1VBGJeULSE6dQfUUqzhZnbT6+4zlFEa+Wn6Kk6k7XAh8C8B8jQ64qQDonk/bt7QtnQpqekDNqg6YnJ/9Lz9RtxyH3Y+fmnnzAeRlTJpBh5fDryQR7fLkoKLRvuWAqLMZZYF2Iu3D880hmJkplpfOI2e/q+0rnMYRK8/+od215J6crY3VFF5DA5UkgEnzGdBjRyipQkUUqx+bmhy3Lm9XJBqnYXP59fUVKTs2TZNeL+hnUabQbm65UkE+XNLxT2xOlw1+wAUqFGyWB7Xr58IURQynGZn9n3QM6FqTuxJ49Rlqk/komklJC0krQUiYf7nh/91gNaKWJKlJRZXhf6fiAG2INnXn/GONyTk0CLkS/PP+OuOLY1UktiHHasFfz1v/G32MMry/ULdX/HeflzfF7eHoTv+fjzf4LfS8NQmMR8WTCyx02OkheeHt7zfP6MlIIQE/uaoMIwHNlWyfXs6TqDGwZCqby+rvR9JfgNIUCpzP392LIwVrSMjRswriMkQVgj621mNU2RaKQgXgOpzORqAYMzBoQhhu/BIYcGkiy5IKnUApfzK4KCcQNKglTtI99KzW1+pdTIsu1s64bRmcFOPNx3OBdwnQESPgQ+f/oxVTrGYeJ1+8R0HFEl4UPC2oHgPTnvHI9HcsqNv9JbfPKkXFHGkFILNI1DzziMvL7e+MX+DzkuA0c18Xg8cnc8QGom41Iz1nZQLTl6lnBFo1FFMrhHtN5IOSGExvtAPw0Ip6mqreatXihk1mVmGh+w2pBixGgJJXBZEs/PM8PBcZ0965qpQpKywPaWlCNaVK5r4nR4oKQK1bFvlhwzfa/5xcePfPPNj7m9fibdXviy/pSqCkVIatnZ5htTb9HSkZLhNGjkIBjvB0JUUBKPd++Q1TC5hd//XcO6efYQkdogRbMwCypCWH720y+EWNlWgTYCKWFdCuU60w0JKWFbA10/kFKi78c39bhj8YHXy5XOQdcd+NnPn0kFjocRgXiDGP2zX78Rh9waw2lyDMOJnATVGWJa3iA6hWno33TdqjFK9Mi+zDhluMWFbY7c0sxgH6lC4NONp8cTSjrEnWXzHkTrG4Y9EsINIRWKBgESNXN7mZGAGRxb8qScGVxHqYIY257r7uSQIuPMka/enUhZNL6Jgt3PbLcdrQ1ZVEZZ6CxU4emtQsrCnma6sUfFZqlQCu7v7whpI+WK1YqcWsrPKMvQTUS/ElNh6AYG12S5fp95vBubM2lPmKEjxsjhMKClJMvSlIcqMo0DFY3j3+ZP/uiPeXgo/M6/avnzf/iJ+/v/i325cHsuVLVxvrzgBse6vPDl8xmpBKeDoOscqoMUE+frM0IVQhB07oCUlbuHJ2K5sWdPUQWpJCFFwl7ojeU0Vn7/tx8JKXO9HohBEPOG6zQ573ROUxLsMVBToJbEdd7QSvJ4mjj0lfp4pNc93378wmBH9hARmSYAk9+DQy6E4OHwRMS/FW0Lh2li2S+NYCsSpbZGjdIgSkYNI1ZXHu5+G5ElKSjGfiSnxJYFy+0MQiFkoh8mhmFEibYle7x7QkqIcYO+Z99nYqpI0W7q0a/tC54z69pKzlpJ4q64zReenxeGceB0PPCLn3/k/u49WknG4URMidfzC3GH+5Nk9TPd6JiXK0io9UzNGddJol8w3USnFUlLXOewnSF6jxYSiWQviaGXCBL7HtFG8P7pyDhOXJcNGOmMRZA49Af2cCOUncf7r9njlZAkx+4/4n/6P/53vv3ZT9Dyd/jR03/M8d3Cx49/wevnC999+tTMbxbu7t6zLpVheOC6PLOue8PU6UKIAef6RsXVcL3eeH3emIYzj+/u+fZjA5ceTwPr2up+c/UUnxh7R987tBLkoKjCIlRBvMGfSi50U5Pbahy60aKYtxtKSKwZqErww997z/0+sS0LKVVqlrjue/DgWUslecnnyxemacBqy5xi2+ItCyWDzgJFx8v8mX5wUDTL7YYyluNh4vHpRAqZl/N3bPGGNhWjR16++w4lJP1YeHp/TwiZEBekTC0uWlegcDiMSCkxpmNz1gIAACAASURBVMN0jlIlVAm8oJTAGsNhPDEOR1L8DqEUzy837u7e8/VXP6DG0ArVUXH327/VqmxFknNmvm6ULMlJ8uW7F6ooPDx2jd/tVsICqs8cpo1SK1IYblts+AbtMFVxf7jHWgtU1u3GJV14frH89B+94/xy5W/+rZ67wTb8c2pfhxQC//4f/Bf8n3/yHfv6yn/wt/8dLq+v/L3/7Y/4g7/z75JNR+dGvn7/gcodw3hAGgmyEPbM4fgBvydCiMznmZAiXz1NhKja1a4qfviDb5Aiv4lwFX6P+NkzaMvUDVxuN9aloGUhpFujAciAtSNfXp5xvUYgmeeFoXsix0qpAWUc+77w8HhPqZFjfs8tv/J6+Q4hNEI5wnxrSvf8PQB+Vgp/8fO/QCrJYDPz6ikykqqnlsygHni8/yG32wtPD7/Nlj4ydCN91xNSJuXMOq+4zvHw/oAwjW/9/N0K6chtLoDjT//sEynfeHwcOB4OOJeJcedufGK5JR7vfsjPf/oPGO8GpJYYYzgcD6RYiCGw7TubX3l6OlAQHA+NBPX6+h01FrSUaNNzPr9QVGXsB/pupO86np9vrDfP+7sPqD5itSSkyuI9bqiEGLmdA9ZKjJGcDhM5Wy5roJSKjxsp7Q21VgpOWuJauZ43SjX86T+o7GHj/vGJx/Gel/OFb37vr/Ov/2v/IV+9e/5/qHuXH83y9M7r87ue+3uNiLxVVXZVV198YWwPmhlsI3s8IwQIIZaDkFiMkEZiy18Aq9Gs2IBgi4QECCTYIDYsLAHDDMbdbne3L+3u6qrKrMzIiHjv535+FxYn7AWSq5E8WNVHSkVm5Kt4pdDznsvzPN/Ph1/9xW/z6Y9+yHe/+30+u73js5/+Ce993EH0aCtYVFuUVkQVcX5C4LCJpChLpnGkKCuGcaIfOhCBoRlZLpYY7ambGmMU19ucoUvns3lZoZUmzedcplQRa3Ni9JyOZ7Jk3mykjTy7eYYmR4gJJRWTU49hk8jk5kLe958xKU9eZBAV9++O3KzXXNpm9kJ9yfGVKHKlNL/4rV/BGMn+8AatLd10JCkKRIz4wXHq7xhUS3CQ2QoXB9IyRYxxTnmrimEcEMFxuhwITpAXK5IXW4wRXJqe8RAo85Rn771g6nq0EExx1qYYo7h/+Jy2icgkorXjcDig9AwN7ZqBPhvROtDULS742SU/abIkBTWfxaLvSbIMa5NHQu9AFBPbm4rrp0u0tvTDGR0KToeGJC+YVE2WzviGPLHEELE2wdgKZToUkCQZCIuSUDcTh4c5Lhh8Q2rXfPPbv8RyveKLzz6lSSZ+6Rf+Lv/mv/EPQDrKIqcvM7Y3T/jgwwt/9umnTK1kuXzJ7vhdpnFgCOD62fjR1g2LajVPopsji8WasWvRImKrjOAcpV7QT4HPP3mHMpK8NBA8/TCRJJIgLVPoMNaAGCHC5HoksFmv5shfqjBKElzPdrHCRUfTN1gs7VAjg6JtGurmjBQJSZZSlBtEENxsS67XS7ayZHe+/9L6+koUOVEQxoZ+DBRFSZJpFjIlKrgc9/Myz9ThRSSVFqkShnEg0jL5QO97nI1cLice9geEClij6Ie3MzslCPKF5ebmimFq6doj0WuyvCCJknNb040tWiiCdpzOE3jxOD0dyfIEq+aFfikFZVUxdrNJIjUpQoA0gmVRPUa55NxhMUtsMjt1+uZC37cgOtq6ZVWmXK2v8G7i1f0ti2WF0gIhIMkWQMBNc6a188z8dtdztdkghOTNmyM//MGer3/8K/yjf/Qf8t7TK877O/rwd1DK8vLly5nX7h0hRtp+IGrFuTlDcKyWFavFS87dD9FGMPieaexIbEqRLWjqcfaEpvOVTKZxXqtAUtqc076nHx3bagsiom1AqEi1kDPyOUBqc+quJk1T+n5gHByrsiTRclbiKMlM0Bbs9jtCFKDd/OEdHdOkaNqRsrjBmoSIY+xnhYyxilO7f0RZ/2UD+fn4ShR5jBFpJ+q6p0xXDK5HG4MVOamtUFhGf0RLyzSOCKVmB87o8DFgTTEDhYiUVTUvCUWIYSSE2VFT1x0PDydiUChmwZJC0p/BpIKudlR5AoOgHc9c3WxQKKKHxBrWmxsmP+FEjXcjKibUl0jbjbTdeaZ5LXK0FrM/VJUz/kEUszM0ONxjb/fq6prEKHScrxDvPXkPqQSn+ghSIG1G3x9RGrSStE1HuoxsNluiHzmde6IU/Pbv/Ov8a3//3+LH3/89Pv+9z/ln3/0T3p2gKCr+/X/4D/mXfvXXcNNIBFCS0Y28fvUZv/wL3+AXf/nbnC9vOJ8vTP4McSKxEmFKAoE8S5HMwCOl4vysEALWaI6XBp2WlHGJkTnpVYfNFG3XcPvmHhAIA4m2xKSkaTuGfiS4iCktRbbg3J5QwtK3NW3TMXWKqlxghUYlkcSmHHYNTzbv4eOEjxNKCYSXSDRZVpHYgnF8Q9/+HOhUIPL29h6lE4S+0AzzqmeRWrJsRVAdeZHgfcDJGcc2GyYG8qLCasO56TnVLYiBzeYKqRO0ViQm4Xjck6UpkpGuG1lUGZnJkH3O27d3vP/1ivefvEAbOxew3SKkoOtaYpRoC5d2x6WtyfKCrum52q7wj+Sucjn7Nyc3cD5fcD6wWs2JIhc0ygYECXVd0w+Rm01K2zekWuOkoyxLzqeGq9VLhn7EyJRqm+J8y+Fw5MlmgzJz12W/P3I8KjL7EV98/sA/+cf/GKsDz9YZP3l9out6nj/x/Pf/zX/NF1+84tmz5/z005+yXJZ8/w9/n3dffM63v/FbBOkZp8+52q5oe6gvNW3bENwepTIupxMvnj5jnBz+MSyelUuGcaLuoMo0RWI5uwv7u3ts4lkurlht1sQ4QfQ0zYWAmvFyyyU4xehGDuc9QoEWFhFn8paXnixNGIcLZbVif9qjlKRpz9jMIHQEDFJnTK6n6S4olYGP84ziS46vRJFLpaiWV7NJTUKW5ighWRRLBt8TgsMqRQyCVbFl8gPysTcto55hl7ojS3JMkqG1mo1yLjJ4WFQLzscaYyRFMe9Fny419f7E6kVCtU1wYcQkhrGLhBBIdILQE84HbFLNK6GpIriRZbnEuQnnPX0zK2A00DUTQufMQCdDYi0xQNOOdF3L5dyzXm2ZppFFtp1TT94xhB6bpwxdT8DR9AOTm5fNxj7OukAj2e1PDNOACGv+9I9fsd8fkdKyXpe0k+LFi/e41C3DNBB84Pf/2T/l0nS8efcOiLx59VN+/W9+k6TKidIhZIsbBvCaxC7oWghk9NOIxCKExpjA5CaMLtnt71F6Jl5Z69kdvmAMkrQoCaFnmHr60COAzKRc5U/p+4nz+JYYJ5LM0DQD7blHaYN3Z9blhjS1aNsx0REs7M57pNLY3JHEdJ6KKs3l3NF1NU9unuH9xOHyDmU1P6O58hUpcgGLdYmL88OLGwbWy82MeptA2xzhA4f6hM0SuvEB7yfWqw065AiREfwtUorHEe9IjJoYJYtqhdYJQp+p8iUIT1FYptyz3mismbFqwc9hiyDivGSkLF3d4cLI4XjGZoJVcUWer+jaC31f4zw0Q003aFI5tzXHduD5zfso6/Bi3m8fx8Ay31BoRe8aMrvm7dt7uvGMthDFGYXhw2d/Ax9qHA3BB5JkSbSRw0PLw33P5BKKRcVhr7m7e4XzDmME4+ip64an1wk+EVyCYJwGiI67+wNt13N7e0suJlabFXm5xKY5x/1I1w8zzk0LPvzg61gJu+MOHyJdOyJkIASJUII0LajKCjd11KeG3eFMlmech5YsT7hMI9YqlNWYNMMJaLlQtw1Wp5wvt8QoIBiMKFgXFZfmiBRwOD/w8r2PefXqM6SGsjRYaxARxrpHaIn3kQjsDm8RMuAnjwSuNy++tL6+EkXuvKPvjkxhIHhHqgsuzY51/ozgOpqpZ+onsmzL5M5IIUiMfkyFaKQKjwHZltP5SF6U3Fxt6F0Noqe+tPT9QN3esqgq8tQgVEDqeTo39IGmGUF2eDxFWpDllifqGm0zmqFn8j1SKYpUo6QlUxtOlwPSVkghmPqRh/saJS3nrOHp9Yb7yyd03YCbDAMjddOwWq3ohoHl5pppN5ClGVMcWRVrbKo41RdGapQ2TMIwxZa6k/zu737K69cPlFWG8ynBe8piiVKzOmX0gf3pwPHSMk2BD55sud8fORzP7I4PjEPH3/q1j3nx4dexScalecXxdE/ddSTGkOqMmI6MPhJdQIkUrQxKai7dCZWKmeM4SrZLhTaWyeZ4LwhOIaLEioQQI+MIu/0t1XLO0G43T3Gjm5+lpAAv6XaelU54cvUBdX9AaYVJMoqywCYBbQXeR9I0x/uJsszmSKBN8N4R40S5ucJoSW6LL62vr0aRO89x1yPVPDrObzSX445Kf8T9/Z4QYewdRX5PXuo5iT95tE5ABJzruLm6ousGrrc3pGlOZlImSu4f3oI0aCHJKkViBZdmR7nYMo4DNs0IkyctFJGJ1CyRzPfyITYM08wwGf2EUoaoQEvDMBzJk5QudMQYyPOc6+uCVbVmdCOXo+PtFz3t4FAR3LrD5pbbN3dI/Y4n189QiScwEafI6Ac+ffNHWK1p3IH1+j2+971b/rf/9TOO+/FRBub45P4NSiesVxuWS02e50Dk3PYELxknR5la+qHn3cM9l7rmfK5JE80HX/86aZ4TJTzc/4C+mbBJRmpSwhi5u79lsVwQo+B0nH2qeZ6wKJcsiwUuruhcy+3xAjqS5XbuHgXH+VQzxoik4FIfsGlC349EAmiBi9NjeDkQZcBeSS7sSYaUZjiTJoY01RSFISsMIQac80QceZ4yjD1a60cgqifESNMciTguqvnS+vpKFLkQgiw3HPcdy0WOUZb3nv4NLu2OYfCcDw3Pnz9nVS7ROjC4hsJsH1EIDm3sHD5GIo3Cu5aAYPI9WluqYkWr9vgYyXVOuijow3yPjvCUZcYw1Fibs8zXBKfp+yNRSPq+IzE5aWrox4ZLHYnOzwqXqSNLljTtkQ+eGj73ntu7L8jTBflqicwsV4sn7O/29B3s9md8HNAW0uSMkCPet4ytp2nOFGWBkKBNxuvPa/7n/+lTzscOaxPKVGEVuHGg6UeapgYizjmmqeflkyV3xzNFYsgyw+u3b7h9d0vXj49OU480hhA9aVpwPJ84nzpS67ELTbWsOLY9HkdaKYrV3CePwaGV4r79giTPaMYLxuacm3vyLCcRkiIvCCFQ2BVC5iBGQpyznm7qwes5ceUV58sOjyTGGUWySVbYTDO4kcE1LKoCCKAEITTkNsF72D8cqKrqL25Z+r5jmhryZEHT/Rz0yYWAtr2gdURIy7J8n/1uzxe7t0QHH738mCdP1tzvd0wDHE4Rm98z9vOkMMsVKmqsnc8CAD0DLjaURYlUsF4/o3MHFJ7cXuPqnkW+wdEhkaTFlsE3tOORqR/ne1plQUbqbscyW6DlDBsNXiKcnzOMoceonP2hp28NISqSNKceD5RFzsurD8ik5OHhzDh4pslTiJz63FBUlohBppowBdp2oBlaijynqRvGfsLohCxJiNKx3W4xJuXzt+8I0dO2LeM40PcX/u1/9WP+zz888mST0Pbd4+9TPQaYJWmVUy4LhqmbDRlLQV4uUWKeRu4udwglOZ1O5Lml6zqqqkDJlLad7+tttuZ0PrAuJWIyJFWGHz2X/oQkZRjPDMMdSupZYSh6tIb3rj/i/vQ5EqhWFeAYejd3WIyAOGGtBCHohg6lJEoIyvJxGCgixbIEEZBCIqXFOU8II8bmlH9VuNBfx+FDIEkyVCZI0xzwKGmRQZNnK6qiYhx6VEyJBGw6zQOeIiEJiuki0Gpm6jXHM9parjcbdv2Jzh+RGMpyyTQOtNOI7nuMzLic9phMkpoVeAc+0vVHpnFCiGyGG6WSrFiQJHZGvmE4XG5JzII0UeRlTnvpOB8StITcSGLXsdzm3KyfYkJCkWfkH2q+FrfzB8SI2cQQJoKYmPqJUYNzIBGIkHI+7picZ5ocRa4os5zowqMMVuEjDEPPNEHb1GSJ5sMXa8o8oyxv+M1ff8E//+4f87v/xz+FEPiVX/kWq81mXmUNR1zsyYqUzXrL8fg5i2S+Bz+fmvmBfbEkSeb1WSEFVV7QnA9YozHGQKfo+55EJuhQUhULLsMdcZBYKYk4jue3JLbg09d/wGL9jOBGrJ0pv1LP5ADvmPGAZkXbHGdYKZF+cLM1epihRZktQESInoiiEwpIOJ4PZNnPgRhLCok2KVpJhmlCK4FSEx+++CbjcOB4uidJCqQwHNs9SWao0pI8XXK5nHGqo24vmKDwg+LSNpRG8JNP3pLnCxZVheOANYb0kQIghMDYlLquaXjLanHN5dKBVLRtg5ASKUcAhArEuMTICp1CmpTkeY4bavq2xzWCGCLrdYEXFkNC3Z1pXU+u12xWW/p4QHhB3V/m3KpKkUjGEJBpStc4hDRIEXnYnfn+d2qGfiCxhqdXN6yqCj85huktMC9+TeNEms3zg93h/KhiNxR5xdvbe27fvePJdkFRFPzGb/5tsmLFav2Mc/sJRZZTlgvOh3tUKOjGA8V6yZMnC5quxVpF3bR4L3HjSJYWj0YQh7Yjq1VKWVUMjWfqIm0303hXRUkml3jpeVu/QlpBqhOMFPRizgpoaTD5DOQnwtgFNsVzGrGjjzuUkExdwAsoixXjMLBZPacd7nlcMieScKk9iyRDyZ+D3RXvHfvDO7Q2JLZgd7xnd7rlXF/48PnX6U4d0yRYba5AglIBEQW701tiiOR5Tsswu2ecZprgYX/GqDXRzdy+Zqgp8pyqsLPRIfZ4rzidGrbXV1hbErlnkS8Z2nmXOURHmuXYRBH9HFRW2rBcLEmTHJ9qunYW15rUzaiHeiBfJDS7FmlydDnQ9Q8YZXg4HLGlIY6eLj1TJRvuj1+QmWyWdJFzOB4ZxyeczrOdIk1ShnFkdzjMXaXEUBQ5tD0Q8c6TpDmfvt0xDg2r9Qrn4XAZuFrnGFnxt3/j17m6fspyeUWRrzg2MIwN8dISRkPTHDme9xx2HZvViqzIGTqHdyCEwk0DTgu0kcSgGLsJ8JwOJ5quZ5zgxbMPcOPEwfXs3Y51ec26uqIZLkQix+NbpNIEH8iygvP5Hudnq4aSKT968wOyXJLmAiUl1sDgpnlYpOF4eYtUjigNfTvnV7XRs00u/ByM9UFQJAuUFphUc2nfEYTj6maNTALPXjyjbhrq/i3KjBAzhslTty3L5QY7GVS1JMgjrW9Z3ixQKs5txGGgzDOkKdF6Hgztj3f03pHYlNVmSZ4XwMR2s0EKx83TNS7ANAwkiQECD4d71tV7GJUwqhE3toQgkFGx2DhsniNCxGvPu4fXrDdrTJYR3YRNcpTWVPSkVYGKEhkCShmuNy+xk+Gnp89wrmVwBe9eZ9xcvaBrZnz052/eAgGrNDEGvGfeu0lT2q7GO8cf/Mlr3n+6oh33FEX1OIr3/Obf/R0+/PrHZPniEa+XUpVP8beRvndIHFH2aJNSNwNDf0SoE2luqZYlSni22wV5uuFyOoPMZ5ydZEbBxYjNJKfmHU3dIFQCIeKGB5p+wvuG5WJF17RIFcmKhIBGa4sUCW030oxnrLF0Q00/zJZl5yakspgqx/VnhIFudBBHhtEzTjBNDnwgMT8HYiwpBEkayMscISQhwtTOctZ6uMWKDanJOLf3s7HNT7RTQ1FkeEYmDSJEhIQkKWmbnmfvPaUZ7tlsC1wYKLIVEJBqTqQbDNvFU0Z3RMZIPxyJesRNHaiUOA342uETQWpL0jxhvX5OlS/5/N0fk1cZp13P6FvSIhCEZ1AzG1wnUFQaFwYcEwv7gp4dm+uSvu9ZFS8REdrhjIwBk+a8+OADmnri1U8VTe0xRvGrv/ZrfOc7v0/XDUilOB53FEWJEJpEG5ybh03DMPC6bbBGs11lOB8ZneM3fuu32V5fz5hmOffTA7OJw9oS5440TceyWpDYHrt9wqUduNQtiMjheGJZ5WhhGMWF4/GM0oLJT0gjSa2hLBO6YaSuW0S080nAO764uyNicf2Z96/fw48dMpmvBi70HPazMU6bhNKssNLSTTMSOkZP0x5Yra5o+iPjMKF8nEMbNsHouaMzTBOT6+HnwTShjcaYjMPhjjRfMLmeaeo4H2uSrILpwiKTdGdJPzVoO852iSzleL9nUVasii1J4lgUljybSHPBeRwIwiC0Z4x7RBQ0ux4vIkhJVAPROXwY6VxH3zuia4mhI0TQUtPuLwjdQHTcHz/hfLkC2VE3R97eznE2m8+SqbatSfUNbdeAGhBSEaNjiA+MrkMHjURwvtwTJ0ndHJGFRKgaKSY2Vwvu3o4kSeByCfz2b/82RWL4wz/8Hm3XU6UZSZoxOkfX9/R9j/MeISXT5NifatbrBcVqzS9+9AF5UcAjHlkIiZSKrrvQdg9IEWZ9SQgMrqYol3NUbb3k3W6H9w6lNT707C87/BhI04qua7FJwjg52m7A+YFxiAQ/WygGN8uuVpslIlq6S+SPf/ynpFnB6AcWy4q+78FHrhcLfPAQAlZV1M2JJNGEICiLa6TWDKOgaUfSxFDXLRRqTnDZASEnjJEokX55ff31lPGXHyHC/f5AUSzxgwYncCMMvaNtoKsbuKm4ufqIN7evGMYLL569wBpJKDLytKTra8oyY5pOc4RMXLCZwtFh1IwfVhRIGXAMxDDQjXf4OIePT5cLoxdcLbbESdD1I/V4QSpF37QQBV19YnI7kgxi9GRVRpZJ2v6A1Rl+VNzt7snylKGf79GjmNgNn5NnK3rXEqbA3dsTWs1h3qvqCuc9zXAkjwkP70YEKZtFxeHdG5rTjiJNQIh5FVXApb4wDLMmMISI0pDYhMl5fJS8/OibVMucGByReV9fSoWPkeP5lmP9Q4axxRrDonrCpbljmiJS9EjRslwqrFnTDx3d4OmHnnJZcDy2EAKZUfhx4Hg6oOQ1OMV6m9O0LeKRKd9dLgxtBy6y3F7zxatbbFLMVLHMslzneOEZ3MDYBl4dd1gz81q6rqFaXHH37kSSJkyjpG2O+N7jupokmy0Yk5tI0vKRafOXH1+NIvcOISKlfcbrz1/TtgMffvgSOTXshrcsVwXLKp8Zek/e49i+JrqRITgW+ZLdw5E8FTipkNqgU0U97BmGkcEFNqtrLpcjVbrgeDqSFgnBT3Rdi/eK9eKKVo6MfUfbN9wsXzB1F2To6V2PDxNukjPSWQiabsSksMwt/djhwuwzGgbHan1DkVcs8oqmfZhXFSJMrmPsJo7tiEgq6lNLahYc7lv62FJ3DcfbnON9TmYlPk788Aff51y3PJwv+CBmxeHugXEcWS6XpGlKCB1JYlmuKq6vr/mN3/g7PH/xPlLC7nBPmCbm/EbEuZG3d9/h3PxoXnozczJeCPnIYYdpHOa5xTQ/s5hyBWLe+syyDCVgvdyS5wVaG7Isw08D2giKxTzGb9oT1ljKbU6RLqi7EzdPVkilaOqOLBW0zYXL/R4pDSrm8/qzEHgn5zXlpsFNntN+R1ZkJCZDqkiaKIxR9H1L8IJgJJP/uUBSwOk0cN79iCLZzG5Jp1lmC4yNs3u9GOnHA0JFUmuwxhLQHB8OM+/vxROEHxnpGVyNTRL6dmJZzlruVfUc7ya2Vyt6V6N1SmIsIilBKMqyBAUugvMTyoKKkKOJccFd/cB6sSJGi9CSu7vXMO6JamIYeryryZICEQXWClJV8bb5BGkE+8MJow2uDthyzc3iY358+110FNz1dwxTT9fkvPpkdspP/cAwdGy213z0rQ/4s89ezWdjNS8tFUVJlmXzZuPlzKWOnC8Huq7lax++zy9P35572ag50CE1bpppV3l6mZUqlEjtIQjyNCPicMPcMvUxUtcHhAj4YPF+Xs6KISCVox/eMUweazVZlkA+w/tn/uMI/Dl8taauO4oiwaSaq6stu/t72nYAEeeWrNb44CkqS/DQ9XM3RgjFsqpIdIYPs+zXJJIkT+i6DuegWhQ87I+sl4svra2vRJEPvSdRJVMYyHI997Hx2AzOp54qqeiGnqp8wsP+MxarEqkjuc0RU+Tly+cIE2eyanuhqpYgI3ku0WakKm5ww0hSVDTjmUwVhBgoiyXjpAiMBDHH1IYeXCbQNlImlmmazXDrTQlhYvfuyM2zK8JkZ4OETEiTijbMstY0F7y6/TNuPv4WUkp0zGi7CCFyOraoiyQTF9pxIi0cebKm7458/mOBjJa67hnHnl/8pV/gX/5b/wq/93vfQet5KSmE2TRtjGZGTs4yWGMMxur5ClItuX37jrx4HGYZi9aatqtx4URWdKhxBidpaQku4oImCDHDPE3G+dKwP57IUkskkKYFIUwMQ01RpHPO1M36wuAG6ubIMPSU2QbvJqbJzQY/AlYrtFEkaUHfNyyXJXk2kdiCw8OZPLfoTDBNjqELZLmewUk+oJNIaQ0PD0eWy2uOxyPH0z3GRJbrklNTY6x4/F385ceXZ/n/mg5rLOtyRWY0eW5YLC2OA4M4IZMBaxLyIud0uqB0iUwS+uBoh4Yxwt3xDSF4hMhJkicYU6GEJc0KtMogeEL0aKNx0ZMXKVYb2qGn6U84P80tKyHIswIfe4oiYZmvORyPKJuhk4Gigo++uSGv4ONvPeXm2RVlUWGt4frqOc9fPMd5j1UV7eXMOBiapiUGweQNlyaiRMInb39MkpSUZUoz1OyPAsKSJElmDneIfPyNX+CHP/hDfvqTPyKE2WuZpum8pOT8bGaIEWtT8rykLCqKouRHP/oxn376OV3XcTzOmL050T4wuh374xvudztknPmSUTlMIpERkmTFw6GhbQbcBMPg8C6QmPm9h36gb3qmMZIYi1GaIi14/8XXSU3JZrElsUsiAYEisZayzJmm+QpxOMzxRKnmodVim2MKg01TVALSRrQ1s5dTwugGgoByuZmdagAAIABJREFUsSLEiegHrrZLnj55ghsi4+CJUdOP/4JME/9/HlorqmWJNhNC93gx4bjMLvXC0A1npIocmpZnT75G3b1FSs2pbplGWNuMZriHsQJt6eqAUBPeR7yf8O40d0tcjogW5wQCS9McqZvz3J0YFVWxZLMqGcaJgKdKN2w3K5SAotgyDDWJyXF+xGY5TALXt1gtuDQPVOVL+uFIkZc457DWUBZXOOF49erEssxRIiKAZ1fPaKc99WWiP1/NtwMxoKTmo1/4Nt/9g+/xRz/4Hv3YAQEp5h65MfPDphDiUVc+f3VOcH9/T993LBYFdXPmww9fzliPrkYK8K5BOksic968eUNRWLpx4Ob6Gd57lNYktiS1JTfXzxn7lnGYmDrF+XwhTVcYrbmcJrTRrDc5w3jh0h/Ybq8YZUNaRsrVe9T1mTytGMcJrQ3KwGq7RMYw2yWGZl5Ge8x7jiNMfURJgRIJUUw4FyC4ue04ur9Y3trvT8QomEZoLjWb5fWX19dfRxH/rMMHN+utx4ahaRHSUZQJSoMyhjiNtHVksbBo06G9wBjAa4qbAknAhQEpOoRbsTt/isp6tCwwQjN5h00S2uHI4XDPNNQYvcQNjlQu6AePFinNpaXILef6RF4UKNFQFkvO7R4fFdZsUMrOyfNxoKsH+m7i6uqa7eY55/OZ6+0VXTcSrWdhC1QMRB+5vl5SVpq+FZwvJ5Zby6s/PTK0G/AFxiQkSYY2Gd///vfY7x6ASJLYx+GO4uHhnnHsEVLOOIcYgbmvPI6BEAJN2/Du3T1C3lBVCyY38tkX3+X95x8i5G7G17lu5qAjeX7zNYbhghIZZVIx9RKUZvQzk/z+4VMW5XMOZ0U/nWnHjsnNupO27em6mrSck1Uxevr+zNDvSc2ah3d3uOAJMVBUGQ/3B+KUUeXzFWQYB7bbitP5gHOQmQSdKooyIzOr2QqnDZfuiCoTzscjPkSUmqkAbggUpiT6L6+vr0SRCwl1cyTJJYXacGkfmIIjEQl5dsOhPyO1o24OKGlwBHyYL1HDKMmSGa5vTYaRFfuzRGARMUFqZsaiUtTtAZtkBKHxXtBdPM5P9JPl0pxIMlhfQ5Gv6IeWXnp61xE8JFlK9JoiKdFlzmdf3BKD5tnzG/Is51hfKBcK51u2yw9o2guNeyAxFcoaykywXJXsd2/4+Btfx2YXiqLifPuEGOfx/KW+5+HhntPxwDiOxBgQAoqifDzLzxY659ycXjIWpQU+OKRQhBjw3vHu3Ts+/OglXTeQpCl5tmR3/gFKXDA6sr8cQcCyvOJwvCVNM2SUlOk1/SSo+x3eeY7ne7q+A7FjuSl5Yl7ig+d8uZClhmn0oHLGaeChvmcazwwD1LXjapWjbYLSjsJaYpAoKqrlhv3DHTZL0MbS9xOg0CqwvbkCGfDjhNIJdXuEqLFWM7QduS6ZosDoCaVqJIpFsSb8jHvyr0SRO+dJ7ZIoW4If8E4hRESLjFPzjtu7I6nJuTQ7xilgTEKZGnwMFGUO0RCcAwNf3P4QlCcxa/ruhJaGvCjQNmN3vKNuGooyR6MwSU6YJEPbIIWnKhPu7+9w04TSGYvKsr+7kBaRWDcgC2Ia8a7FGs310w/xvmaZvmSYPqXpHgiAGI+cmz2Iiba9ZXJgVELdnlhfZQh9JCBxfcXlHGmbBh/CzCIx9rH/HeZbCKUfL/l/Pg2cb3eknDXezjmkkAQRmKaBcVTkeUrT1KRZwesv3mHShHp4wJoaLR6TO4cD07Ugs3rG74mSn7z6EYfmHUkm8VNAokmykoDHT/MU1sicznRI5ZFKIMMM9fRTwI2RPMuQaOr2zFW2oa0nTg8Nm81mxv35kWVVzgoVJqIXtMczq2XO1HfYXFDkJX0/EuxswHs41XTdRJnm8xZjN2CUwSWec7vD6J+LLURFxMxjY+MZeocUJX0fMGlCDGATRTgluElQmATrDXu3p5o8IXZoI8jSJVnSkmSWKAJaGYzRnOsTk3ugrx1SJAQvsemWMQaE7DifG7714ROicExjTzMOrKuc3WHPMASm0bFYJigp6ccRHRSr5VP6vseqjKHrcGNP8HA4d7TqjNbzkGsYHE3rCJfx8aE6x4eOPP2Q88OcRh/6Ee89SfbYKRAC+7i/YW0yCwlmwTTGWISQjyAlTwwBz8wFF0KQiDk8vVqu2W5v8C7yJz/6Ds34QF62rKsS70cW5QIVBJmZgwindgcqYG2KQM4UMB+5ebpCCE0/eI7+TAgCjONybjCqxE+R1BQUVwI/lcQAxJFx7GCau0tjX1Mfe7QqGboRLSXjOLE73bPZbNluNxglcZ0ixkjPPXlZ4kePmwSL8pp3dz9imhyLMpJqjbKKxTLDhwn1M6r4K1HkQggu41uElux3NU+e3xDpkQlMzvPivTXee57c3JDYFe30jppAlubsT7cYpUhsSpnNr6svNctVjtaRu4c72i6yXC6xRqGThPPpwPnhjtXqOaM7842vPaOsMiSKpj9waDtyGzhdalZVzuFUo8WCoetIomQUA8GP7I8nKruizu5ougtGWbJ0SSozhK5x3YJpaBh6QZoacrOktJY0cVwunvvdzBl0ITB5j5hmsKnWBo8jBE+MEWMM4zg+ako8PkS8j4/hhPk1SimUUmhlqMqSLM0pyoqXacalPfB7//cfzNpA02O0II4eh+EcL5gipxkm0jSQ6pSApygq0nQ+y+dpQT/2dFM3p6Imw9hZZDIzc4R2KG3ReibxhnBgOAV8Nwc7YoTT/sR6ucFmlipfEnVLkBVJBmVWsMivedi9pelquq6h6x2pLinSDXeHz8nzjNOpZmxGnt08pUwTHA1TaBDxy8v4Z7YQhRCpEOL/EkJ8TwjxQyHEf/L4/Q+FEP9cCPFjIcR/J4Swj99PHv/948f//9rPeo8oHEJ6pJGsr6tZgZeaOT3PRJbP1oJyadHJQJpaTDILTkMQGJsgleZwegeqxaSCKDrybIUUC54+uSErUtqx4+F+j9aWJFUsy5T3b7Z8/PIbxACv373mcNqzXVuqQrFeJLRDjzLwfPs+T7fv03QtdXOmbi5Mw8hIR5pB20fe7XuaekfT7qgvng+f/U2uls8wssSGDKVGoqhZFltck9M1I8M4zsBKoRjGCYQkTTOEVEhliDHi3OxE8iEghEAIMcM/oyD+RZRMYY3FaE2SapSRaPXIlRaSzFQoZr+nVBpjUoahAykYxwGjAzhozj3j4FEKLucTxBmwFKZIbi2bxQp84Mn2KVoZxl4z9RltPRFiJMpIVqUsrwqigDA5UgWJTuZFqxAok4L21DE0PX3bcb4c+fGnf8y5Ocyyr2zJOAbqtuH1209YFCVGaDK1IoklMWiOhwveSZp64v7h+KX19f/lTD4Afy/GWD/6PP93IcT/AvxHwH8aY/xvhRD/JfAfAP/F49dDjPFjIcS/C/wT4B982RtIKR/TIgngudQXyiInuJS2a+n7I8QBlUusKUAomuZCVS5JsxwhHFoLxnFCiUBqUrwfMEYy+YlhGGjaiUVRMdiJokioG8er1z/hm994zuv71wQieZFzfXXD5DrevjtQVQvO5yPr1TVD4/nhJ3/EolzNLS4ZmUaP9w3nc8P5PJKmKXlRYBD0U8/94VOWyTXhap7qZZkD6bk0R16/Bu8DIcA0jXg3MY4DRZ6RJBYpJcF7gvdImSLEPL2Mce6iODcxuQHvIkpLEmUxRlMWGXmeEKNjmgacl9R1h7Y5WSaIsSNJUobO4YJAG4NUcL3+GofDO/pu4nw80/cjeSW5XGp2uyPLZM0QLafugSdPnhLigBAjaTYXGjGy3VxxvrzDe9heL4grQ1II1psVTT2Qpznn05Hd5TV3tzvyRUamc871gf3hTJ4UtEPPcgkxOmRIUGT0Z8vxtiZNDFmactydcaElHgeQkTT9Ky5oPdrc/lyUaB7/RODvAf/e4/f/K+A/fizyf+fx7wD/A/CfCSFE/JKx1DTNPe00SxgGP++DOIdWlrafkCoio0Qx0sUJa3O26ydoo+jG40xZnZjVK0LTDZcZINQcKcuKw/6OMEmsdDRNx2p9hWpqNtuMS3dCqHnHe7lYcP/wBcoUpOmGt69PrIuX6DihRYKn5lQ7nj/ZMLkwuzSjQYmS1TKiTE/bNXzt6UekyRP2D58QHATZY63FKktEELGcL7NdQ0gQBIKfIIbHwUmcz+B+QkmJUhqY6bbjONC2DYiI9wEpFUZbtFaUecpHX3uf9XbF1XYzTx3dMOdb26cstwfKZUrXDrhBMISAkpLVMkOKgDRQmoRhr8lzy2pZMPaSpj4TUxiHQHMRvPX72QShQaj5ylIkSxJzw+3dK/LcUp9rJPPJ4NydKcuCZZYyDin9WKOt5t2bE1kuKWzJmRodNZkq0GIGGClRMPXw9vML929akkzzoFqqylCtU+qxYZwG+uZfACZOzE7A3wc+Bv5z4CfAMcboHl/yGvhzwssL4BVAjNEJIU7AFnj4f/3Mv9COV0vNarGlb8+cjkcQAicjbd2AlHg/8v7T5+R2jq21XYNf5PTtmXHqGd0JITTOO7p2IksWxKi5uz+w3YwkJsGmJUPosJllv6/Z35/JrGK1VqSZIcaB5aJkaFc87I4I0/Dtj3+V2/vXnOoHwsrjfSDNBEJJpnYkCMnhOJDpG9rTG3Q2MjnPoX4g6QOLxTXjdCH3G7QteNj9iGxhkGw4H2eUmhKPn00Bi6rEJgnOOfq+RevZXPvn9+ZSSoZhQIjZtGy0QkqF1ooiy3j58n3ef/k+1zfX3Nw8IbEpzk0sVyv6/gaT1RjtybYFY9EwDilFnqCE4ni6Y5x6JudQGtaLiqJYc3u+RyAZwzibl/VIEILbu1u6xpGtFFplVOmS+nJk6AIiBqL3pDpHkvOw36NuDFniSNKUm8SyyJc01w297yEInj/5gMXCEtGgHF3b4/vIosz5+vsrnv3W73C7e8efffYj+rZn6B0322sigqlvH0vwr1DkMUYP/KoQYgX8j8D/Q92b/OqWpWdev9Xu9mvOd865XURkZOO0M9OZZYGpogpEUQMb1RAhkJjxz/BvFBKCgbHEAMQQ4QmoGBi7cCV2Ou3IyMi4cZvTfO1uV8tgn4hiYIVLMpYi9+yce77vnubda6/1vs/ze37wb/O6v+U9v4od//CjNouU2NTXuBn2hxOrds0Q9ggZsUZR1hpkpm1rUGHhdfvMNEZCkgzDiPMBKQ3d+UxVthAlIlnKVjOcHffnCy+e3zL2ke7Y8fJ7Hy7qQd+xWhvePzzw9pczbobra8Oz3TOO5/dMvkJZuN29IsUL1iZOeWS1aRmnPW1zTdMciCqSQkIpwWV8Q2Fv2V9OjOc3FFVB09YU2vB4D32XiSkipaQoLHVVLN7FvBC8Qlii03NaWqxSymW8r/RX0tLZTeQUKVXJze0NP/jhD/jgow/ZbLdcXd3Qti1Cecq6QYoH3j/8JSIHTqczQkaatl2y6gFjCxobOJ5HinKFyBmjl6eMGwOn2HF1taJt1pyOPUJJpITLcSaGhMoPdOMJaxSgSUkyuEjXDbRtg5t7epWwusQWhnZV0qygnxQhO+qqQhDpxxMySYyCZl1ilGWeO7y78Bvf+wGUM7PfM/mRFANFUZLi/49u/ZzzUQjxR8A/AbZCCP20mn8IfPH0ZV8AHwGvhRAa2ACPX/e+AskwOPZDx8NhRNuS7tKzatY0a8P6ahFJFYUixMXcO46OsZswpiSjELHAj56yBmslwxR4/vKKyliUjoyuX1bIcRHb/+RH30aVGZcn3r4ZSWlDzBprJbvrmtvdCqstlU3cfvc3COGMm3uePbsmxJnJXZAykUXiOHzB+4d3vHi1o2i2bNo1j+fXjK5hHju01kih8CkxTIK3b5atx5ers5IGazXD2DP0A33fkXMCFrNDeroZlhtiyQfy3kHOT4dQw2//+Cf84Ec/ZrPeUjcN2+0WqTRVpRFSc+5XPB4V93cPSJNp12vO3YmYwM0OITzJO9ZNA1Kwa14iTOKDj7aUVlPYcpFlRsVqUzP0HQ0FdJL7R4dKF+qNYXtd4aNnGAKXy4BRievdiwW4mp+QBLpCFQXaJpgvWFPQ9wMSyfnoKQqDMQqfI8fjA/OcWZmXlN3AcJpJMlEUlmQkWc4cz6e/W5ELIW4B/1TgFfD7LIfJPwL+c+APgP8K+J+eXvI/P338L5/+/X/7uv04sPzBncDSkuPAzfWKHDOlLdheFTQryTj1zK7H6hopNEZLbq6vqIqWafK0ZeJz95aqhqosOZ3PkGAaHOv6e6T4r3nx7BmVBm1q+vGIypq7wxGfFOPo2F2XbBpD22wpa7ukr9UKN5/ZX0Zun90S4sRlfFzw0OlEYWsupxO3u28xjHvWJcR5yR6ap4HNquV8WKi8p/ER3V5z97bAuyWDPqWIkoIYl6zSaRq+WsWtNVhrmWeHEMseXimF98vHxlhSjrx4+Zwf/ugHaC2QCqqqQmu7UHonR4qRVbOjahRaXzG7gRQcKQRO+yNuTigtKFXJdHFUa4toMw+Pv2TT7rjelQzDTEQhlCSnTFnXFE2J1CP7x3tevLqi3mikjpjCUFWR22ctYZ5Z1SuMWtjrQzeQCBRmhxKZymzwyePdRPYakQVtvSJnhZsgOIGbHJeuw5ojOtfoUhNNj8Lgg6Strv9uRQ68BP7bp325BP4w5/y/CCH+HPgDIcR/Dfwp8C+evv5fAP+dEOKvgT3wX/5t/4HWit3NjrZu+eD7twTn8SkgsyLEM5999oCUmZubV1wuj3g3U1SW0gYcgbKukCbz4Qctdb1GCUNTbDkd7mmrLX/9i19QViVhjtwdLhRFgRSZ63XJb3zrt9ifLlitEWqirhskI3OQpODZrn+TT17/CcbUTENAcYUVkduNpBt/RaFXGFvhZsHc9cjaMrjAPGZEJZgzmHKNVhKlJPNQcdovIbQpLWP7lNIy2o9L50TJxapWWIstCqRU1HXDs2c3ZBK/+OQTjsczkClKw4vnt2w3K6RatC5aq2VoJAVSZGJOFEWDsUumUGE1bXXF2/FzzocBkRvKGrKMICTuDL+4/Jyb3RVuyOQs0LYgx8SvXr+jMpbZC9pSokv4nX/0ETJJlI3M0WOyZNWWjOOZ2xdbJJL+ONNdBtb1DT/7+ad8+1sKKT2b7RVTd8dmt8PqAjfPzHPk8DhxeBhJXqJs4nge+MVf/d9olalWirIWyGyQhSWnv6OROef8Z8C/8zd8/hfAP/obPj8B/8Xf9r7/3yuR0EVPVAbn4NJ1X5l0hcysqh0xBfquByEptGYcB3QBMgfO/owt11SlZFVXeAdGehp7RUqK66s1Hk90EoHjZveSYXqgako2dcW3Xv42f/WLf0VlGyQeaSxCVigEv3z9V2QBbvT4FHn3xV/TrgrKqqAtP0LqhJsdhZG8er5lHDxCabbth0sKghR8cPVdMGdqUfHpny++z5yeMnNYUjGc+3J1NqSnrcmyekvW6xUfffQR/+jf//dIKfH8+TP+8i9+xul8YLdd8+xmiy0MRWmfDpuOEBwpJ5yfyXlph52OM3GKvHp+y/3+DSJFpnnE9SNSXCNEpNQl1+tb+hRJPjENA9pahJWM88Cq3i6A1mmgtIm5m2hazZw82gu6vmeeJuoqopTAe7dEJ+oV7dWWbhrACo7dgWe7HSGFBe6ZIqfhHUpaVFGwvtLk2KByyWU488UX7/n42QuOpwvDxTP3ME2LREP9LSPPb8TEM4TIft+hTGDygaEbCXPi+tkVm3bNuinYH+9ARoQ0qFRz90WPVomH7ojUmqr1FKpGTT0EQWEUvhC4PGJliRUl+/6B69uWrj/SrguMkQgks/MU1ZJA4XyPNoZCbZimPdc3G3wq2T+ekLpivdriQs/jfs80JcpGMQ0Tv/uTf8Cbu7fEHFCyYBw7pFEYG6kaz+QMfSe4ewshpK9W8Zy/bKFGjDHUdUOMATdPZBKZRf/+7Pn10kVp1vzoRz/k9uaa+7vXKJF59cErhBC4eWZUPXXTME5nvA84HxFy0W9v1mvenV5zvpwJQSApqAvDtinw0bPfn2mrQBQTQgesKjHVMohLacth3yGC53yeKcuWopRovcBPtSo59YnkLIRA0okcIsPQ4bzjPN9Rl2va5pYXz57j/czhfIRHy7l/S7lSFEXBoT+zamva9YrWtpRly8OjwLzUNKLlYb8npsQHHz+nqg3/z88/w7T119bXN6LIc85Ms8D3R3JUXNc7prLD2BklZy6dZ5wHQrpgTMv7d/c4H5nf7ilszfXmhkt3htoh3cCqftIxLw0LxnFgu37Fs5eRfjqDzgileDg84OpIDoFPPv0EqwuKVeTVM4uWPdJmYg7sjw+8fz+w3qwY04EsNaZq0Frx5s09ZVXyyy9e8+r2O1zGz6jXBT6dOTxI2lYzzu/RVJwfLG5WCCmBReBEVvTdQFFYlFIIURDCMgjSSkLOXG1XkB11ZShLw+76it1uze6qYJ5G2ralOz1Q1SXj4DF6EbiFEJmmCWUMMc+UVvPhBy+IcyAh6N3MutL0Y0dTGVbtLX3fYW3F5HtCSIupolwCEGLMrFcVWSiurrZo64lpBlEQc2a3u+VyvBCngBskLo9IvWy9qtLQVi21lSiV8XGEseKDzYdYW3DoP0dKIEu01Zy7R9bFM+72XyCMxjYWP0bMRnCzWXHzcoutDb+zg85fvra+vhFFLoQkxonrzZb+5Hm4P/Hso4YQZkbX8+7tASMN2lga+YpSG5Qc6C8dConWgqIsqBrNalVy2u9pmx05erTUiCrTj48YozHWUlhFZMLamtE55vEN19dXVHVLUdVoZTie3zP6R6SQi9zUJrrpTFEalK5IKXPsPM9erdjW1wg1cJmOSOMxBRhT0JRQG4sxhs/vPufh8cWTFMFDVsv3EZdRfVGU5Jy+6oUvaRmR2lZUVlNb9YTPEzS2IKXIZrtl7A3WaFKYmHq3cL6VoaheIKSgLCoGd+Fh/ynj9IDgBLlmGiPresWxG1it1iTvuXvXUVQFj3cjPk4oClZrxfGyOPg/eLVBGMHmukQicFOmrBuyEOQ0oZRnc9WSfEAEzXCY0U8hVn4OiJxZ1RVpGDFacvBn6D5lDgPrqxbnHVlGzt0R5+CT9z+jKVuqyuC9RwvLy4+vqKqSZB0P3bulgu2vQWZQTpkpOsaQ6FyHaSUxReag+Oyz91Tmmt/87g+4f/wc7x0pLvvZuiqYXcSPkc3aMM5HyqLFaksMmZDONEVL188IrdjvR2whCG5AqEDdGkIcKItryBmtLwgROXaBbrgwzRNXmw2ZjJawqjech0eqesINBU3dsL3d0j0+Yq3keDrTrByHhy9IaWESJi85XmZEWnF61MxuRCAWrrpfMMRfOt+dm55iYJ5kCt7jvFtYhFYjsif4mUrXCCnZbK8pbElOgRQN0XukXG4OpfSyJdKKOPacTn+FyBeqqiSomXolOZ8njocLtn5SC/YJK+sla0mvaDcGbQVbLUF7drcbTt09hbFIWXCOirauF4a73S79+wSSghgyunrGOHeUTc1w2bNpa3ShsEmSh0y7EWw2BXNI9JcTVtfEsqS/nHFOst5cE5zHuUR/iTS2YNaeyExSClUqhMoYtf3a+vpGFHmMke4UmccTIkaCyojs8XnCiIIiljy8f8PkZ+bQoYzmarW41XfXNXUl8D7iJ8tsRmxhuVzuaVeWKALGyieh0kRRlFzOlyUFwS2KPl1qmqpiGs+k6HDBUZYNhS3xfqSqLcFnUvbcPt+gsqGxCdtUNErTftQsvsTsmKaOxgjGKVBIuAwzTVtyfNAMnSCERPCOlBbdilSKVdsyDAPeP6EVBF8Rbd3sQAqM0Tzcfc6H3/khSmvsUyvRe0eO6d9oWxCUVfOVRS4R8OGBdSvoO1A6kYEgQWrD7bMtZVXx+Lbn5bNbvJcUlebm+RafI94dubl6jssd5ERTNgQCKTuuN1dIqXl4fEezyUsIVlzzeHiHkIKqbBarW39hHEeGqqM/njlfHtGmwioFytOdHjkdeq63NUpUi/PJO+IcsNpye/0Bw9hx9/AFm03Dumi4DBMJhyZTN3/3FuLf+6WkZNfuuPQOrStePb8mxhNCbng3PpD1RO8GuuFCkPD8esPupubt24GPv/MdLscDfT+yahZt9DCPCAOmXPTLUThScrz88JYUHS/MDu9ninKFEhu222v2+ze0dYmPnqvtFSEOiFSQc4mLIyFOGBPR8gojtyQVsMGTzQBaIEJaMudNSWFqpmmi6y9oVfL55z1vfvkBKTlyzozjSIyLZ6soChBLBil5iRL8sndurMEUBUlIfv7pL/jRb30Xayuk1GQys5tJKeCdJ/p5kdqagrJqFp4KEh8dSiZcOBHzRMqGsljjzjPXu4Zx7mlqi193NG3FqRspSohi4P7xPQZNX52ZY48cFx2NqTXeJ4J0pFngJklRKERSmNxQm1cch/ecLg/0pwFUpl3XfPH+PQ/3R2xZ06wTVQGKGhlrtlvL5mrL3cMdyY28uH7G4bEjJ8UXb95ys7miKhqksfTjQMieTbvGp5nP7z752vr6RhS5sZoPPljjQkYVhsNxT86OxtRcP1uRcsT7kRe3Kw77CRd7+k7Srm7Zny0+K4RMSGPJPrM/PuDiRN3USCWxtgIUpa05XfZEJNoK+unMrvgu/aVDa8Xkl7F5ypnSlpA0MWeCg/WmXLyhqsGHnq47s2q2uNzT6jWjE6CWIKnRJS7jIzm37B8PnO5+k6ELSClIMX3VTYFFIhujZ5z6ZchS1sQQQCm22zVCZP76rz/l9rrmJz/+MTFnpqFjGgeM0aQQSU8+SiMLVpsdyKeg3ejo+z3ez3h/BhMZfWSeE5vtK07dI6pQxBwoGgskqsqQpSDnGWMFrp+Y3YypKoQWPDzcocdM10+kaNFixTBAUUlCDAsR4HImpInCVqTkKcyiew9RUVW3SBHx00xpDXcCSzarAAAgAElEQVT7E5fzgW9/+C18GqibklVTk9TMbWtoix2n44VW15y7iDQJIS2rcsPj6T0hjvTzr8HBM+XEsX/A2JJhDKhSMPSOME6QIle7LT4WrDYtu+stx2PH42NPWcI4fM6pu/D8ec3pcuDl7fc4XQ6QNUqBm5Yuwc3195lch8Dg5oGUZrxP0PTsVj/iPP6cwQ9Pw5ISLWrK8prL/EgIMyZbvDZc+gtz6CjLK7p5ceoPl5H9+bCYF7ShH0ZEqhaG+HzDPBX0w6LcW/yZ7mmkv2hQ+q5nnieseeKr5IRIgmHomecZHz27bU27vcV5z+V0WDDRzj8NjzRFXWBsRVXXzM4hVIELmXkeGecTKUXOfYe2khwHyBqkRAi4dJ6hy7QNlLZA5IxLA9tVS2oCuhKMU4dVLdoWXC4XxtkjhGKKHXXd4tNIDpayjLRrTdcrYnI8e3mD0YksAvOcaEqBspCFWs5LlwNtc4tWFilB20j0gWl0lI3l4h9JOqGaK6KbSRLa1ZroZzIeH6Hvvr6+vhFFrpRkvdtwOi6869W6prSGLN3TI9xhrGGeJ4xtcVNitd7SNA1CZMappx97jIUoJq6ur6nciRQy5EWldz4/gKzp+xGFoq63aKMZ4pnL3Z/w0fOf8P71H6PEYtC9mz/n+x9dMc8z0+S4Xl/hJkdKHmvLJWOo1JxPBx7uL0wusVq13JSWobsskKKw4fRwRXc5E0MkpoAPnhC+FG/KpxX9qXugDOM4kFJCa402C/MvpohQBVIZXv/qlygNbdOitSHljNWGsm6omxbn/ZKiLKHrekIQdN2FaV7621/NBqY7tCgwqiJTMfYzOTjU1ZoYHN3cUbUaaxU+TOyPD1wbgbaRK7ujnEZiUihZIAgU1nI+91TXt0THAtKPEyF2NOWGyTmurlYoFtqvlC3RedqmJUWHNRYXLst5yCqE1jjveHw8YWRDqUeqQnO8HElp5Hp9y/Pbj3n97q+J3n9tfX0j4ELz7DkcjyAqlDIM08Ac+sUcEBUxKu7evWHsR+4f3xOSp64VWhvquubF8zUqSwqtGIdHUk5oLRmngXEacH5mf3zN/vCGYViyORdR2Mz+8cLxNPKXn/6U+8cz7+4ekdKAqDgcD4zDtPSN3Uzf9bSra+bZUdZrlFIEZ8mpZXd1i1SWyXkkV7jxY/bvvoUbCy6XnszSs/befVXkKWdiSk9gJBiGfnEAxfgkr01M47iM/JNg/7jn/v0bBIuOPMbMPI0obSjLCudmnFugQ8F7jocD58uFS3/m0ncILMSKyynhJotWa2xRY3TB1U3LzcsdpjYEPGVbgcqMkyNnycvnryiEfZIhTCipSWlGm8Rm05JTYtWumSfH6M/YQmGokHhMoRmnZfIq9KJe3FS3GKVoiprNpsVUCiklm/WGaR55eHjgch6wqsbaFTEGrClQWqKFxqqSy3CgKgq+++0Pv7a+vhEruXORT37xBevVhrbaktS0CHqy4nQc6bqZ5BWuCozjgDEFU5DMqUOaLboM1EkCgvvHPd/73r/L4+FTQsq44ImjZ+gmqrrGqgY/T+zdmRgM++NEU2sUmW37inE6M4cZrS3DdCSmBbYf0wDEp7beTOje4KfAOCqk8QjZcjn0jN0tx/dbjo8Cow3z3C1RisYyTB35ycIWY0RkQQiBEPXSGk0Ra+snhEZkGEdSzgghOZ3OfP76V9xerzCmRmnL8XBEqwVZEWNcOi2wYOH6gYeHe0I6MsczoJFSM0yOojGY0hBlWoqOiLKZORxJSS9Mlb5H6sDN5tXCgI8eFSwxGhAspoqrmwXd7GbqZs35dKTUJTGrhWqVDTIXHO7v2dQ3uBFynCmU5Xx5jxaWqvBEGcnxgsqB7tghKBAMDN2i77nZZYwQlKqisS2lLnGTgwBudKzazdfW1zeiyFPMZL/lfPRcNy3ZJKZuZmKEpOiP84JpTpIYNc16iQA3yjAMd0urLE14Jwgh8+buc4wReJcYJ4+fxOL+7z3FpiKngsNhIofMzc0OwfKIf/v2HU1bEPf3EDPfevFtejdyPp/58MW3OA2f8Hi6IyXF/v5Ef+kpzBrvFPf3j6TxW2T/bAFapkzwga474+YZgVhQF0qhtcZ7TwqRqBYbvhQLSYonPHOM8QlJYfAu8Hj/wPFw4MMPnqN1wTjO7A8nXr16AQhmNwIZrYsnyFDHu7dvUGpkTL+iXWuGYWYaPM1a4fxIUVkmV1CZFiEzKcolatFatvUKqTM5ZHzwXOaZ8dSzWtWU1RIAUJQOqxPnywWlFbZSbLZbclgTwvL9FbXgzecDwS2IDxEyUhVElsN3zAEXZtpqOaSuVy2zS4SyxirH8Txi5JIQ15RrDpcHJj/S1iu6w0gKiyzj665vRJFrrSFr1m1LVW6YCGQSUgRubyyvP4ftdYtzmdWmYbtdhg3jPGN1w/F8j0Atqb/SEsKMD+BGcJNlHBLH455CKY5HR9VqbjcfMEw9w5g5Hi54l4hRobWkbQ27qyuMUTAJVquWrusZ+54QDdM4IkRB25R0ncNNcHn8CD+usMrh5rCIk5yn7y5PmZMBrS3zvISuLkW8BLIOw4AUEmuWw6rWZjEhk79aoWfnmKaFKRii53A488knn/Dq1Uvc01NAKcOX+/xfffYrutOJkDqef1yj9IwxglyU5OS5XCbGIQETu6vFND3NZ6qioCwrwPL4cKFuFN044GJimpatVjkpNrcF83yhLCu26/UyrS0MKU4cDmfquqAuKi79wG53RV1VKCHwfqKuI6VY83C/RxhPloa7+zvKskaqAFkiZMAaw3c+eImQCa0Ep+FzQpwJQVDZkpv1S4JzKPO31Nffc/3+W1220GyuSnabhnIlOD96tE1oucJowW//5BmJgBsn2naLjydc7EhYHk8P5KwxskWYhEEiRbnsvZ3CyJaoHdFdOEfBZmMZx4R8LshOkWYoyxV911E3BVVrKYqWmCMPp/cotQiojn2H84vmZZoCbb1Z9umXkuNdy3iBsoyMzi1QzrikQfRDzzRN1LX8aqJZVdVXh0vnPQKBMQWzm5762xmtisWZD0+uIDifLrSrlu4y8tkvP0dLg1KaEJYnkZCZHD1933N/f8c4jjR1QY6SYqNp2hXdZcJ7jZQsh/lpWfUzCv+kXNRSIkjM0SGngnEySN2jraCoFsseefGbGmURYpncppAZoiOkwP7YISO0T2kXOXq6YaQsKsZxYp4GtNasmh1ZOoZRkZJe+JTDhaF3rFc1mxrO4wPWanKSXC4zwUf8DJv6mrEPZH4N+ORSCl5+0NBWGw6n15RVQUoWrSzKKspii/COVbnlMpwZfOA8HNFGYnXN7CKzC0zzxGpVLatgApJGyMyrFx+yvx+o6hLnZ4pyQz/M+AS6MJzOA21bI5QHZnyoGMaevjuz227ZHy6gHCA4HO4p7Iqu64EGPzRMnXpCRUBIgTB5Zufouw4f/NNqvMDurV1oT1W1APALo7FGEpwkBkFSi+w0xIh+YqkIIdBK8vHHH3N99Yz7+58jpeTm9jnzPGJ0Rmv95OJ3nM9nDvs9h8Oef/i7v8fndyek2BOcR8QlB3S9rRmHmSQbqjqjTME0ThRlxFSZzbpmHgpEDKgSEgXHQ0cC2quaeepRqmaSM0YUpJiIyZOYqUrDOGR8CGSR6ceRsXMo1JIm4QJt06B1pjs/UlWGyjZ45dnfJ8piSypmrG0QwqJyS0yZcbgwnGeULDhcOuY6UJqG69Xua+vrG1HkQghWzY5h3HO6nKiKFUI5nJ9AXXM5n/h495t88vovSNqjdMmm/BaJYdG4jCPrumZyHT6MqADSBmpjkVKDDnz03Wt8sDTlGmthjh3tusW5hNYNUkqOx462bjBCEYWmrjdc+pFhHDElSDJNtWEcFX5e4fsdcTJYOzBPE4fDI0ot2vB5npinaRnh58g8D8Di9gkh0DQlq1XLNJxpSoVMmtIWzLNmcpGQAkIt7JRA4je+9zH/9J/+R6zXO37je9/jeDwxTRcOhwe2m28TYyQj6LqOx8cH3r75gqoo+OEP/wHrneb+8r8yy46Xr55x7geknkg6YdcBU1jev+0QSJrKknwgOkfbXEOyGD8ucYPS4uduYSCyPJGKUmNyw/v3v6KoCrJaOlDOLSD9fjzgXcLPGdsYUvJUrUWZiDSC9W7F0J9Zr7Z084UXN69oN5Z+mrh0M7/64lPGwXF1vebxcKSpd6yaa47HA9c3K3LKRPlrsCePMXLuHrh0B5zzlEWgsg3n7oLvA6qw3B++YM4OKRYaKslS22vc5UR0lpM/0g9xidguBTlGTANKGGY/UlSGRpdUpWC7vuHxWCBVwNiezaamLK5YbwaOhz22tFR1YhwnQohc7WqGYSSna477V4yXguAEUgjGYeR0OnE+HzHGLPLWeSI9HRy998QUmFNYFIFjT2FLqqqkHzqm4UJtG5rGsBaKmGCcE5NL5LwgK4yp+b3f/4+pmwqtDR+8/JDDt4/8yb/6Y1btd5ndtGCclaYfOu7ev+Px/o6f/OTH+BT4/vf/IcPP/pT5dCTlmWk48fzVFX4KCKmxhaKqJL/1/R8whUc0BTJp7t7tMbbC+xmpNeRE21SURUHIEikDEk3IE0VVUhQlgx84nS6czxNWGmyh8DHy4tktWhe4ecC7YQke7jqcy+Qw45Nn8tPSlh0kIJFGUVTLUyIHifANWTfklFlvC5AjooD7w98dLvT3fsXkOZwen9zogsRMokBJgRtHSrGiE2es1UQEl+4RmTXzkEgelNRUpWYc9JInP/onZ4182hKsuByPlFcl47SEUsVxJlUzRgtmd0Gpgq5/j0Ti3QVTCdbrFtfMBG+5HF5w2e+4nBMCTU6B2Tsu5yOn04EQPH1/wfkZgcSYgpwXs3JKCYkgpYWEVVcN0zRxPOyJYaQuJNuV5cXNluCXKd5lcOQkKOuK7/7m91mva06nR0IMXO9u+e0f/4SuP2Ot4nh4oKwqXB+4v3vPmzdvqArLer1ingba5ltcrT+i635F3Whu8jXPbm6o1I4pXICIeaZoWkWd1wz9zPF9wk+B/nLifOm4ebFjvdbUhWYOPcoY2qLBxYWcYIoK5zw23yAz6KzJaUaQWbVrbq9f8nh4j7UlZSuY/RkfPFqXGFswxx5jGs6XntwlNptmMXDrSLutQDhuX2w4HzuCGLCFpWxKDucj3g9fW1/fiCIXKLqLAwJlbej7GUmkKAvW11fkKRO8YFOtOA0H4lgw+Uhtl4yZui7Ic0mYznSHgK0VPihSWjI4O9ez3eworGJ2M4fDI6ttzeF84MVtS86Cvn/31QRRiEDKS8vtcir57OdXnI+JnHsKUxJyh589w9B9NaGMMTG7mRg8TbPC2uJpFV8Oh1IIQvCUVYkx5mngk/E+cbwMPL9ZLTjjQrMqLNu8DFeevfiI5x9+G+8cburwzlHXNbvtDf/4n/yH/MWf/ynjNJFy4peffs40OU6HI0aB1WrZ8onEbvsDPv3ij1Aa6lbg5p7NVY1/jKQIq6bh/v4dhVX0Y0eipTudOe49q43l/vMj/UqzvYmYQiHChMnD0kN3BVlktGlQTrB/PBJzRGoYxsjz5xXd/B7nnwwcbmKcBlCKRGBwI0pD9JnoDHVZIURF5sA4n9Gyoi0abF1QlJa6lry/v+P8/oAbYbP9NZDagsDo5W4dO8f11TNS8PRpCYgKY4kbRvTz3+B8iIhg6c5HdNvSXaanToVHlwXrbYUxBY+HI0oalF4oVMgBqVqiCFgjgIgWglN3YBokwVnquiZET0YisuZy0Pz8pzXj6FFSE2Pg7I7kpwSLlDLWFggkdSNZrdZYa5/25cuJf3ZLHIxzMyJDVddoYxjHJUgKoRimQCRz6EaKskBrxYtnL7i62lHULVJkyrLAmgIhFG7sGWxBXa/4rd/8Hf7sp/8Xf/nzn3K5dIyD43I60hQCYxTRefw8sV59sHRX+iNGLREnwzRwGfpF+y3BzTNnJEIYUj4wecfsJuxssKYkesWnv3zL7bMGUxj81COSWBoFOeOD4/A44tPAemOWAp4dk3ck56nqFhcd51PP+TJgihprltS3Wm1Y1TuG4xe0suHxcMQ2gavtC3JIbNsdwQWM0Ux9x+32JV13JlYCU/waoJu994QvtdHJ8vbNI84NNFuFkoa5m2mKmtdvPydm6KYessaHDKlEFxI3RgKeYY48W22xultO+u2az375hpfPX7G/f40SFpJEWonIBjdKrG453B8Zh0dAoKVGseJnf+Zxc1oEVWn8N5CfGJ4Cqsxi11KWyU3EmOi6jpwz2lgKa1m3W4w1pJSWmPAnpso8Tyiln7LrA7PP7N8duLpasdq0SGPxKVNrQ8oeLQuUVovMNgXmeaAoKtbrK37y27/Lz/7ip6SQOOwP1FXJ7a1C6S8byJm2vqYst0+ErBWXsUNIzf54gRjJEdp2zTRPrOs1x/5MyJ5yW9LuDK+/eL8w5IXl/fuRMPesG4uVhqoVlK3GSsGqsZzGmcmNZDEvT63kMaVhdp7oJW1xS2UCujQcjwfW2xtqsyWTWW+ueDg+cn86UNVw80yRU+R42hODxKr1gugTgbppCC79evTJAcZh6Q27QXI+dZjSoqdEd+kgtsRi2esezmeUgapsKeuSLANCgCpG5jFRFhXz1LFaN6AC9+/OdGfPF+HhScwUaFcN5+OFru/JCDbFFZcucL3dstk1DF3gX//xmeNePDnTxwURV9ZUZYUtlgOg955Ld+JyOTPN0xIB+ISTkPPEJBXGGJRWrNoVYx8Yh57Mctj+Ej2hjOJwGjmeOmxRcTgNrK8c2takLFHKLLpzMil5vIuLZbBuoai4ffac/+T3/zP+8A//G4yWzNrz4ce3WCMwxQLul0jW9cechoF+7BiGCaVK6mL9FBAbGJ3kfHLk2DPMAy5LQuoQtmB7bUkBzqeANgKjSsY+odq8kH6nyGZVU2jPet0w+QxSoFVFWdT4MOOjw+gKYxdA6Tx5bnfPQIKPE6M/0/kRu1a8bLbY0iyBaIcjLi+ir8gAKjLME2mKKGrE3yLQ+kYUeUqZTXXNOPQoBEJYjGm4HEacS7RrjdCG/V1HlBlrDEpJysqSlSUzL26dUlOXDU21Zn8+cjlFchSU4orKrJmjQwlPGB1df6EbHHWxwjQtL569QCSBnyQP7xKf/eJAWbacTkdijGyvbrm5uUUrxeVyZn94WIp7GvFPv2Sl1HLIlIvSL8XInBIFFu8d0zwveOOYkELig396jeT9/Z4YEt0w87jvePYiovXEbvfl7yhhrWF2I1WxQklB3y3kKCG3fP/73+ef//P/lP/hD/57qkZRtxVumtFSMU0dIU5IGg77M1IpxrPnatOw2V3xcN+TXMfo/QLpzOAmz9V2h1ICNz/FexcF8+TZbloUgeOxo2gt61XNNM+07XaRMWePUhkhC6SCmARdf1nIBGlm9iVV0zBHR2239MMd0+TJKOpCIpSEJzjqPA2IIICENhIXJ3w6AwKlKsbpQlO3X1tf34giV3Kxn3XDniQjty9rTueJ7VVNjBXOCd6+Hdm0BaowSJ0XQX/c8/zlB8xjT3COOGeUsMisEVQYaemHE7VueXx3YIwdm02JEZHZK7y3dE7wf3z+v/Pq9oarqzXDJPjLn47srp7T9z1KadpmQ11VSwpwijw+3NF1F7xzhCe8MoAsC5SyS+vQOZRcMAtSCs6XMxmQYnHgZ3gyLj+hm1NYNDgxcTxe6DvPZm3xfhlyCanoTyNlWWKsxbml3ebnkdkYpJD87u/+Y96/v+Nf/p//I1obstbk7Bn6I4nE4/4dOUNpNMVGg5p5d3ek6xxaGbbbAjcH2rblzdt7bOFwXaBpK+q6RJlAu24pTCJ4TbPdUVaSwkg++OgFfT8tXHSTmF2iKFpccPTDBW0KhAykAGVZMc0jUhuGaY8PPUJBoVcE5zk+PpJVXghisuLm6hXW1Dzu74gxkbMkZsE4jpRFyTD3X1tf34giL6xdMjxbi3MRHzJVrahqS8KTzxNVJXj+ass4L+PlZgVGB6xORJlpd2tsKvAu0+3PjHOkrWtsNoTouGrXtEISxcjhMtKdEs5JrHaURUkSA4GK06MhpxXrzRVSaYqywhiDlODczPl8ZOgveDfjvf+KfKW1Zp5nYsxIIZBCIpUikZlm91UahJQCF8JXBd4PHe7JujbPMyEk9ocLh0PHB68+pOtGhJBkxkXjIhWv3/wSozO31y8pbImbR7RSZOCf/bPfox8/heApmpoYJ8Yh4tLIpX+7+EynmaoSOD+idcHNzRVWrjGqYLKPSC358KMXhKio60jTLEiLopRYK2iqLT56YoR5GjCF4v7xLbYowMDgZtpmzTiNjFMks3Scck64MOK7I95LjJlxChIRhXmKbLQoVaKLgrHr0JXhMozINDNOE0go1BUPxyND74htIqZfg2AsgWIYHH2f2F1fM/QePztSnkmip6oN2iSq9Yz25ivVYdEUPOx/SVPt6MeB3s3kpIlZsqottaw4DXvQApUyc4iUqwYtJT6c8THQ1DVVa6mbRHdY0+/X1KVnmgacc0zTiPMTWkku5xPT9KXhOH3VSUkpL1k5UtC2LW52TxPIRWBVliUhhMVI4DwpR6xdbH7D0D/9PJmU4HTqGMeZN1+856OPPuZ4cnT9yHe/8xHWWk6nPcfDHW1bE4OH54Jd8YKcE96NZAQ/+sF/wCd/9VNWTc00dUwusz+/5vXrz6irJTJRlwpNwexGpMzMsSNp6IZAVcIcjtxcP3+6WSXKNJADMY4MU0TrhuA8Rhv6qUNICDFw6XtSkkg5PSXWgbEVMXjqas08eaSQKASF1tiiYH9yeCJeDqQI0hisLck2MfaLGC67maqp8Ek+aYEKVm1NVRuEFCw08b/5+kYU+ewmzv1ICoLHhz2rTcu2XXGe7rHW4pVne73DGgjpjJLLStmdFhDO+XRhmhxls2HsAzfXWwqtuHvzhs8+O9CuN3z75YY3r/cMs6cqDZMT3O4aqlVit5W8/bxkvpQMvWeaJqRWVOViyYrR4+b5KVLbf6UHr6qCqqq/WoFtUVBVJYKFCvYlillK8QTpzKQcUUrS9R3n8wUpxELKypGyKDkdz2ijOJ2OfPH6LcN4YbfbsLvacnOz5XI6EKP/KuU454T309L1yUuW0HqzY3u1w/uReepw4cz7u59xc72FvKgh/RN4KCeJ9yN1UxP89AQVLWira9q6pZ/uydkw9Q6jLFJrvIt0/QMkCSItTyX0Eh8ZND5EYjFT6OUJsKo29H1PioGm3nI+9azaliQSUjVMw1uk0qQUKOyCfVP/L3Vv8qvrmp53/Z72bb9u9XufU6da1ynbsYPtGDtUlMRGRkAiZggJhBQYZIqEECh/AANmMM2QWWDEgAmjCAVFlrCNK3FXLpdP1dl19t6r+7q3fVoG73IpDHwIqsKqekd7azdaWuv+nvd+7vu6fpfR6CIt06TowGTQmRygXbfEPBOjR5eK7vxT0K5kkVE1FEoTU8bYjGoyBkFRNuQX/LGP0wvRVnE+TRRlg8ZizDLGG8YOW9SM/gSyZXO54mfyinIluH94Yo6CTV2hpOf28oKi1Hz0wRVCCP507+hOI+PkkVKgULj00mu/QO9zzighkEojLRi9jAbbdklgzjkzvqAlYko/TG7z3hGiA5/wbsLaknHsF0HXi0tISsU0jVijkCIx9D0P9+85nfbM80Bdl+wufoXVaoP3A+M08uGHH7Pe7JiGE0N/ZHIz1tSYokFbyzDu8cFTNYnLG8scQWWDGwNTDMtF0Z25vLjGh5kQzxSFZt0UbJodITqKtMEaTce0iMxiRClFDpq2WXE49TzvT6Q4IvJESp66KQiVxGqzvJmGe6SQlFVcAgzmwKgcmcw47bm6vuPcHanrC6yyiKw59c8YA4qElDNl1TBOR1JSjPOI0hakYZ7dC1PyL39+Iopca0VZl5iiph/viRlCjMuauNRUVUkmQE5oXePmifX2ghQT0zxjdEFpC/rTiNSLoH4YJ1zn8XjWTUs9C75+8Zr1ynHqHON05uOPv0SWI+/eCKZhiRfUemGCi5dTWAiIIZBSIAO2LCmKJUbQObdElnuPkDD03QvjUODC8gM3umKYz6QcGPozxi4IipjmHzr2jRZYKxb6lZHEuPC3n58fIHu6s+Th4R1vP3vDzc0NZdmyq2+p2g3f/s7voQW0zWZZ4a9uENqyXl+w379ByMScH+mmPc3qgtP+iBIV8+Qpig5jBT4cWLUNPlbgPSKPhCgZxwheUGApVcmb5z+jKi1t25JCRGtNYSxXF6/Y7w941/P6gxtUFlxsWryHHHoigIlLC5IFTauZJsf+eGK7rUihQMkCbQzP+zOltKSQQCm8HymrGucdUpbEmGnaCu8jzo2M04iQPyLV9q/iEQJiHqiaGluulxGUNGhVIDFonYhxSUpOyTFjlizI5FnvWppqx+P9I+22pKwkKSYKpXHA7qZGmMxuV1IpeO5O2Lrgqx/fEMIz56Pg/RtLDEu/HGJAKgnpBfWQEuM0Mc/jy8Uy0nWCzWaDsRoQ9MNIjMsY0VrLNA9IEYlhhBxJ6aUFEqBlyziciD5itaBtC6zVLwQvQ1EYxsmhRCKGkfW6ZL01rNaWx+c3FIVcMM63d/zJt/85x8NnkEHkjB9mPv65b6Jtw+XVK94/vKGbv8vsjiihGc8R58AWabmUGwUCtNSMw4zzJwqz5t3DE0ZXZBJuSICkdyPRw5gdpowICTlGtAqcz2e+8OElwbfc3dwsalA/UpZrvvCFHU15gwsdvTsilaCwhslN1KVa6AhtS2kKxm7Cu0hihgxNU/NweGadBMqsyCKScwRRYYxkmheY0tD/FLQrCDBWIZTHKENVbFDKcnnxitP5U5QpEFkhdcB7Qd2u6fszRlWECNMESmuQgaIqX7IjE7ttA6VHkJiiIyGomxXdcMSHGVu0nI6G014DCa0VWnO1vPEAACAASURBVMnlhxsm/OyYvV8c8CGQQiQEj5CS4/FIYS1FsQjJxtFRViXeTZAn5F8YCdIICZQEpS0hLlOZ9iW41hiNNYZ5fnnN+7Qo91xitSnY7mqub3cUpSDmkcPpkXZV8eff/128WwRa0zSSpszPfOkX2W1vyWpJdv7oo4/5l9/5PawtGQ6Zx/c9qtC0dYk1grooCSkQoyPGjPcC7zqMrUkhM/uET44wnRmnM1KC1C/JdErTjwEXIqA5HM9YpZbWRQ5IJTmfHmjqNd97/+1lUVdayqpAqUxVGW6v1oQkmcewpGhoS6oTUhms1szjyO7iZhHQoRYJgDEMw4zWiwvKGPvTUeQpZg77E8YCUeFXHiE0SgXGuWPV7mgrzeF0YA6JuoZhcKzahjBHwjhRqi1Cn+jH80tSW8ZaeDg+sm2viXNCFJLT8zNZQN3WnB5rPvtEkYJgdv2LBsYgyYu8M6dlph3jy2x7GVUt5oQACKRa9OEL6CeQkgPi4tVcwpVBiGXElzzGwtXrNUpp6mbRoqQE2i5jU6ksWkn6s+crX7+kbAy2AGMzOQpM4TgOnxDCgCAzh4TLM24MmKJcPqhlQ06Cy4tXOC/pzwdW20uKSpMRCJVQUi0GEpPJEQ7DmVWzohueKXWFmwPOzTwfDggKikJzOnX87M++JiSPSJr79/estoa23TKME0lYxmmJmimLFdYa2rJBXkiGsyN4iC6S9UwMkX1wCKFxsyLlASkFWosFuSehaovFLkjNHEaELDn3B7SuOR7Py87AWNp2/bn19RNR5DlnZLJoYRicJ0XF+Xwk5J51uyN4OM6BcTTEHDjFZ4righgU0ziwbkoe7z/lg4++uPSBUS8npkpIX+NGscyosyXHiWZdEeYb/uRbZwiLFkWKxWtaFAbv/oJVmBZ3/fJFLgyYFFFCAeIlSDZgjAWfkQpSWgzUmUxRGJRYAmXd7FivK9q2wBSWsrIotbwJMpHt5XqR6WaLH2Z2FyUffGm9fPCRxACrBorS0489mcQcPPPsFq+oCzg/UViNVJYgEgjJh7e/xG//3h9xeh64vb5BGEcmMDtIPuGKjCkKlJJMc0AqTQZ2F1s49Ii9Q4pEColXtx8QQ8KoAl0UvHr9IVnOjLOjKRvW5Zben5GsICtSyJyPHVkkyiozMINYDhAZS4SKVGVNCJ6mqunGI+MckELSJ4/IyzQqRQNJcT4OxJCRZaayDat2y+gPuOmnQGoLmc2qQapM8BkrDQUV3gWu16/phz0+DKxWG572e6qyYp5m6sKyWW2Y+pF2taU7d5yGE6VaIZWkixOZiAsDVmqM0tzdfpVT5/nW70zMQ6YswPmJFAJSa1KMOOde3D3zEgeOQGmDfLlsSaVfuCdhGWPpJSol5/jSe2eK0lJV9gVsI6hKRdtYEmkxacuF0611olmXCJ3wo+f2smRzV2O3DUMYUCZjjaIuFVZquu6IiwmlLJM/cTzObDdbTGMWJnkcyFkiVY21lo+/+k3eP3yLH3z2x2hluH++p7CKyjZYU8BLuxBcJuWeui4Z+pEUM5vNmuJFMhxi4OLikuATQhXkFFiVW6yR7MUB3yemITC4DqFA62Wk+fbhDe1q0ZortUhys1cIEZDeM8aZw9NiTRToxaLoJnIQy0DBWIQwTM4hhSWbGkSkLJvlAxMi2v6Y0t9eMoP+T+AHOee/L4T4Mkso1iVLxud/mnN2QogC+B+BX2FJffuPcs6ffN7/rZSgrCTjMFEVgrZp+foXfoHn5weeh3esNisOe8cwdNhCo2Ti+nLH2A+czgcKUbNtrng43FOXG0TKnLoObTN1qyA7kgjU1Ya3byPf/qPAaX+mKiqmaSR4zzhO/4pMdilyxCIDjWk57aQQOKdIgH9RIqa0sA1toTgejpCXnrosDFarZfGjNeSIlAvgU0uFklCUit1VTVUtEMur6yu8i+RKE8kUxuDdAXLETRqVHXOOrKqGcZ6wBm7udmilmI4z4/zI8fSAkJ7VWmPUGrO64OOv/Cbv3n2CSIrKrFEqA4ksIyJLbDAou+M8HrEKFHqR6cZMnDPSZBSSeRrJWVCZGu8Dp+mZ6GDsRqSy9CqQBYtvVS/3CyEMx+cBWysm16Fkwzz3pKioa8M8D+SUMVKBUAu0KC+Rvv14hqwJIePdzOXFLePU07YNsz8Tc4nRBv2C2/uRixz4L4A/Av6iAfrv+DHFjoPAlJpxlDi3mIY790jQZ3QViMKhrEJLi/cjwQf20571eodIlqqqeff0GYREmD2vLq/JzBxOHUpr6qLFyA1/9seWT783cTyNlLZESs08T6S4+BWbpsF7/7LcCdjCEkNaRFWJl3W9WJYuzhNTpK5rYgz0/YB3M029zPELqyntkmsvpaCwBat1TU6Cdl2z3tZInQnOIdCsmhJjJHdfvMSFDhRUdQW5oB+OnIYTq7qi0ZoYJ6pKYU2NliVZBF61V0gVsaZGyAKtJSFMSG348hd/ie9+55dI8wPbZs390wPKOHJKbOpbjqcDxgrWtUEriHiUlpRWcbO75Xh6JEuNdzOaiuQ7DJlxmtifzqQMjd7ifSRnxTyNNKZBK0lrtwtMtEh0Q0/fL19j1oHD4YzRC20ghYyPMyEK+vNMWWm8AyEdIYAxBqEC4zSQcSglQcgXcsCPYU4uhPgQ+HvAfwv8l0IIwY8xdlyIjDSK7cUVfa+ZpiPKzNiioJQLxqyLB9w8vtjKBFW1ZhpGtNS8e/fA5B2NaRFhRhmBKRNlkHzli3+Dn/vav8Nnbwb+xW//Lzztj1hbYOzCZzFGs1mvqOtFSTeOIyklyrL8f9jXxmnCakXI+YVNKNFSkNILGnl2WL0Yj6vC0NbLkuovcjZtISkLi0CwWheUbUapRH25IqXAel2TA8TgaNpMTJKYJkpbsNutqFvL2DmqsiaLiFJgjaG2NXPo8fOZ4EfeP7XcXv8qx8M9Ri+y4LJq+es//xt85zv/K3Oa2LYFRbnhPPZYK7i9uUKbvHyfpzMyDwgE3fmwUH6Fx7kRGQ1VrUFlohOsask0jpTVBh+mFxRHROeCw+E9q80Fl5s7UnCc3YnkDNHPlEWDlEsbkpOkLC19f2DV7jgezxRFBRnWm4qcM33fk3MguMXWGEPAqJqh31O3K+KPaRn03wP/NbB6+f0lP2Ls+L/65Jw5HvbU1Q1ZvCAZQqAubhiGidk94tOMlCXz7EkEzt0TVhWUdWK7sZw7SXaC3fqG82lkf5745q//A7720d/k23/8p/yTf/I/8fjwQBaS4GfIkeDdsrZPmmkecC6gjV5+WCGCFMgX4H3KGRcDzi/EK5Ez1lqqQjL7gBSBel2jJNSNxVpFWVq00YvASySMkhRVwfpiUQAiPNWqIKSILh1TPyGEQFlBcImYJVklpMrIlHDBU6SIyyMqS/bnPVXVoQRooZnOZ/rpd7h//5aHt55f/7f+XawvSDFSFi2b7Sse9o98ePshU+hYNeXSCoSZOQwMrkdki8iW5AVKCNw4UFYV07xHCsGceuY+sdtck7Pn6uKSlCV1s+Pt+7doDeumZTg7xvOJ4CIySQ79M8oUSECKhC0M51OPlAXjtIxfh6HHmEVW264qfJiQomRVLH5Pi+FiZXHxQF1YjExM3cA0hL+stP71ilwI8feB+5zz7wgh/u7/29//132EEP8Q+IcAm21JYTf4MGJ1yzBGtDccTiPDeMTNJ7ppxppMCpqiqBnGB1LhMKlldhFjaoQQPD7sqaqSX/vl/5y/9o3fxI8j//z/+GfEEHHeYct6GQemQIqeMHuiGzHWkrMiZlitVkyTY5wnzAsnZcm7Dy8gnYxRmuvLiqpSvPnsSF2V1KVFyEhVGmxhFoi+XtqXlBaOSllrhMpk6TBGYEqY+p7RzUzRL9ODsSJGgZCBmOB4XkzNVbOhG08oaxgncPOSQNGNHTlNBBcRuaApLEIrPvn09zkcn7m7/Yjbm4/o52eMrhACpnmiKA1RRObQIcWiBlyizjPg0aZgf3hG9wsTpps72vWM9xP7zqOVJCpJP4wMfo81BsgooF5viGHRnszjAhJqN5YLY9C6YnYntpuCLBJdPzFN0FQ1OS5ty+HwhFYFpTGICIUqWNUVQieeDidmd6SyW8qiIjQ/+kn+TeA/EEL8+0DJ0pP/D/yIseM5538M/GOA21dt7nvBbruj709o3RKcZgp7pvG0+D+94vn5QNOuCXOHlhUyZ/rThIiGnCLdsKeoGv76r/wWv/5rf48cPb/z27+Ld24RI2WxMAmDJ/pAjp6cE0m8tBVFgVSWnDLeLwsKrdQPZbKLKMpQWs3tRcXdq5bvv3mHUoJVW6N0xhqLUhLxQgtYPlAevdg5UQWsdw3WLuq5c3+PVg1CKmKMDEPPpt2R5sVFc36M3O976rbG2gml2iVnqKnR2lLZhn7YM80JjWYcPTNPNFXJpw/foq4Nx3FGHWcej28gT4xS0o8DPlqyKpZCDGfqZot3ecGy6ZrIRFEZ3BQwskLamu70TFPWJG859Y8gCorKUOqaGCQhZ5JOSCVomkumuSdkyaq0GCupihKkR4mS7aqlG09YZRAsmOrVdkXXCaqqpe86hHAUtUUrTe9OFLlAURJi5nTqGKdIVTU/WpHnnP8R8I8AXk7y/yrn/J8IIf5nfkyx4zEGnp7fczye+eIXbxmHSGE1jbnE9Zmn5xNXF5fo1ICY8TFgyhJEpLKG6AV916FMxS/+tb/Dr/6N/5CyKHm6f+CP//BPQGRm7wFBTpmiKCiURQl43j9DgqZuqerVIviJkaKwS/8dA0VRMk3LaW9S5KPXa774hQu+/4NP6YeeqjDUjSZGh5Tih6dhzvmFhCVQyqBNZn2h0QZc6Fm1l9CpJfHYrJZVuKloyg1DXCSm3fiI0jXTnLm9vca5gQj42OHTRNt+xOCOSOVRUtCuLCiQsaKbJlCZ59NbTsMB50eMlcxhorZrUBVTODGPPaYyuBAoihoXI+M8IBQ4H5c9hkn0455C13T9wmwfJ48twPszF1to25bZzYxMGCOpLhoC4xIT/oLIG6eEEJFX1z/D8/4I2bDbXDDOR2K2HI9PGFXQlhVNXeLmmYxEFZLsS4TSrC8axiESdY2PPauLzzd5/ihz8v+GH1PsuJASsuPNmz2rVYlAsH8aiT6hTeB0OjG7RGkqrISiLslZ4pzj+uqGd589sd5+hZ//2b/Lr//av0ddNsTomEeHtYZz30NmWXTkTFVYVtXyaq2qEmvty/xbUpctsw+Laz9nUtKEFyWhklBYwe6i4P7pPcfjCSkzZWEJfka9RIsLIVBSkkJAqWUyYwrF9qYh5JGqWnE8OlyYiEhKWyOEpKgtlSkWnIMt6ceRzfWWofNoO4Kc0YXET3A4HMlZ8dn9PUavUPL8Q5mvD0tQmEsnul5zOs5I1XN3d8M8n0nJM47PXFx9gBsCPijyJKk3K87nCRcDMcyUoiEApERZrDiKM6d+pDAVD49n1tUN0T+z30/EeOTh4cBu0yB04tydSAmEcGw310xZY3VDjpGpc3w2PfDu8T0hT+T4GXW9Q5hAVTX05x7hJxwOIRS7qwZkwihNU9YM04koJlCG3aXm6ekvve4B/x+LPOf8T4F/+vLrH1vsuJKSorDc3dZM44TzE0aXyJckhKZZhPtCZIpS4whcby6ZB43F8I2v/xoff/3f5vWrr5BixrkeKRRl1bBabX64mcxAzAsh1b+4caw1Sz9dVS9/Dt5PNJVdbvbDSHc+QQ5UJnJ3V9H1e54e90zTclF0bsQag5QGIXjJBgpksWAviqKkubLcfblFaYHScuGE14m6WOF8R9uuyKnGWIEyCkdgDg6dI9UqUFeXdP2Bq4svcDp+H6tXnPsD8zQxhsjuckdKM9Yaur5jGhYycEoZpTS7i4ZpekbbAjclikozzIdl/q/ii1oyMk4Lkfdrr/4NvJ95+/ApLg+EFJCyorQaLQUfvX5NN/TMc0mhHVoaYkoMvVsmMrKgHzwxnslJk2RmluCHkfMh4N2Ii47SCtp6tyRNEPEuc729o1pt+fR7P6BsKkKMiOQYxhOTa9CyxuqSMQzLniP8FDiDMpHTaaBdbTgdD5y6E5e711xtLpndEVMIEAEpMutNTZ88KQWs0Wi95fbmG6xWF7x99+fstrfsNhfMbkKXJbuLi4XE5R1CanIWzLMjq8R61WCNRpARLBLZ07l7UR9GhvFMjpHXVxVlFakaWG1q7t8fqBu7yG9TAhbyl9EayKTolxOdhLGG2w932O0SNBWjJ2bN5fUtECmswuR2iY+hxMUjSmqEcKzXhoRnGo/40GH1JV1/JMZF0KZ6i5AJU5T0Y0cMSxpzcAI3B4QSKKVpmmX7+rw/kCVoWRCDJ8UBoRQyLZEz0UtWqwt21RVGlnz38Tus6ktqsUGRsHq5t5RlyzA5pLG42VOUDSknyrqisJLxNCOTXhKUC8P53FE0L/HlUi8mkZixpqSxBVJk+qlDGk9brjGV4X7/XarWIqSn63q0AYElxEyKI1a9HEpRYn8auCtkuL5Z8fCwR2mYBhjqI1m19O6RYQis1ovzuyxr3n72A9S6otA1gztzGh741h/+b8yT4zf+1n+MjzP9eEYKTdXWQF7QcCkhMviYMXrRrLSblrpawl9d8MhB8PB4IPqR203F1UWFsYmoR7Z3NfOU6PqSDMzjjA8JsnixYC0/PKRECYlWkt3VitsvVthyRYoCPw/4MZDxbNZXjGOHkhqXPNJEprEjRc3h+MBmc4tInsQCwx+691RViw+Jq80FZIEPHhd6CqWYp8C+7xBCkKPE+Y6yqujOJ8qqXr6+FIl+iW5pmho/LClt527Gmg1CSLwbGHMiuZLvvX/D3c1rPnx1ydCdGP2ItAZdJEphuLq7YBiPpBQIIRFTQPuExFAWDUUjeT7tMdoyuxmZLda2aJk4dz1qVWFLjUqSOR7RxpL8TKUqxhTAz8zTwKzjYm1MEZJckCRlQV0bhvHzk7F+MoociXcJrQxCwsVloqpZgq7SgNYWoyFlz7HbkxNM04ioNNJ5Pvnk90k58hvf/M8QwJsffAfvPLfXXyITidlRVobuPCJ1uWCV+56cZr7w+pbVaiFc7ffHxagQer78es2XP7ol4JnVgdV1u1zGnKJpWkKIGKXJKRGjJ6fFwiaVREuJkrDZrbn5whppoOsmlJYILFpYVLFsTGMcKIsd3XDA+SMueFzqSVlz7o4oIZHZ0HUT3kNdl0jZcTjeM0wDq+aanEdAoXRkHD1VWZPyzM3tK56fj1xfrRmGAyk6zqeJeV7CtLxPuDm/UAh6brYb7l7f4v3MZ29/gM6Gr959BRcTYx+RbNm2CkhkPVM3myWBWeclllFCChGhA1e7K7QxJBXYqR0+9Ev8+BRZX5akqPBZcR566AV1rSjrGpElbgYhDW6eUNlgVAsycOxGEBohIriOkJZlXkqfH331E1LkGakCt3c7QJLzRMqRfnxHUxuUUeQcqe0GbS1VVaCF4N27tyT/jrvrL/HRl36ZjODN2z+l646s2ku6/sTp/IiPA5udIcQJ0uK3nGf/okvxNHWJ0QnBQKUnyk2maDJnjhTrRN0aZj8jlWS9bYiz43xcZuUpegQaa80CElICqzX1uqS6sAR1InJNP41ItYzQhBqJKeLnCR8j++N7hCwZxnnBRCuD9zPn0+EFiVFzPPbkXCLEHqlGvI+Mg8KNJ5SOSDFijGa1XvTagoqu71m1FTF5ggetlizSeXBsby8WQE80jOOJ3faSkDLnoaebTuxPR2SQWGl5Ppz47HFAmsjF5Y4UPM2lIWdFTjNGK4QqGfxMTpm6XCOUYE7dkhqhEipLSAGlBZNf6F+XNw3RRayxS6K0ijTtjqf7e47dIshr1xWH8z3eGcqyYBg6hFQM/URRepTaMvufAj05gNKKF1kOUiTaZoV3kLNk9ommvECKZVW+Fg0iVJSmYhrg9uY1SY68f/oDSrslZ4GUgsf9G+4fPmG9Vahe0HeC8bwkFecYaauCHD2Xa0MMkn2rmINizCNyPWAvI1kYYpKLlKCsEFkT4gmR1eLimXuqsqKoyxfIvqZta7avFKoJlFXL5AYCjsqUKCsoyxLnHfM4MQ4BHxw5O0iZuTd4KZaZfq55fuzZP50JcaauMlol0DPTmHGTpqnFImvNE/fvO+5e3TFNDms1fpxJ2dP1DiUlc3SIXLNZVZxOHZHI0A1YY5FJEuKR47DcRaQ2xOh5OD9x6iayHhEuoopAu2oZBqhKvdBp44KFi0nQtg1algzTSDc+vWwyzygplztR0ssbWwiEFggmhDA09Rak5zTcY+uCjVXIF0G+zhVVWXH/eKBtSk7nI0VZIYiL3lz+mFSI/38+OS+hqjkJbJkpywohAsoEnPdAxvkzOVuEKUgkrF2MBu2mZoyfIvNMN3h8cFjdcP/0Z5zOB5Lo2V00nE5HvE9L0FTwGCX42pc+4Fd/6We4uljze3/wlr3foy8EN7uWshKEPJKTJ0TNxcUF4zgveZdC0jQVh8cTWhmapsFWFVItOoz2SmDWA7aoUVaDyIhsiXHmPAysVh8RwxIVLpNk3e7o+0gWE20ryGnF6XzC2paLncRow/E4YwrouoDSCe81Slnauubh6R11bV8+ZC3n83fp+5GcCvSqoT/PbDcV85w593vu7m443O9BQGEabq63aAk+enx0DN2ZqRcM08zdzSVFrTmce1arlmEaF+2MLEnpU7QUuBgIybFpr4gxMMeZfuopyxVSJJSoCd4jpCKnBbrUdye0XnJCQ5pxYebp9H20gqJagTJILRFSUFeW9WpLzBM+B5qmxpqCceiYQo+xP+Iy6K/iiSlwPMy0q0RIvPg7FZMLuFkTvEe3S48uRCJmTxATG7vl8fhM1RZ0ww+I0fG2/4TXrz5GUTAMj4zjPcEbBJCDQIqMFpnf/Nu/wG9981dYbUu+9e1/wbe+9y9JdWK9q3Guw2RLiAlrLFoLptmRokIpSVVZ7r/3nlM3slrXVHWBspqiKKg3mcsPDFF4RJbkvNBvm6YgpkiWgoTkPBwpq4pkACFBZgpjSUkwjhNts0NpyTR72lZwfXdJ3wfu3zukkhhdcPfqkqEfyETGcWS1sYzDnpwUwWmMLRj6GaMtIUieno74JHh6PlFWDc5FjNasNw1d/4TAUChQWiC1pCwM909vyblhniVFGdEikzoPeDIV0SdMBXXT4MPic11yRjXbzY7j8UDGUVY10QtGN6O1p663GKHRsuA0P9EdnnAhokzC+TPtumF2AucmQvakMeFUDykTkWQpl+zP1BJj+tz6+sko8phpmzVuHlBacNyfKKwhhETOglXb0rQtPs5ARplEYkSahrYpkVbi5on9+BlalZyO9wtW7PAJWQiUTTRVg5ETUizLnH/zF77Gzd0lf/jdP+RPHv6E+q5iOA/IJJEy4wMUtoLssWbDPDnWqwuGbua563n7rsfHTFMUWFOitaFqNaubGZ9GtJFIBMNwelnxL/Qs72aej+/p+p6qzAhhXlR2BtOWzFMixIndZUt3HFF6ie+enKOoWl59UODckhBX1Zm+86xXl/TDicJsOJ33DMMEyTCOE4UFF0bkuaGsGuIoUbJ8AZbuqZuS2Z/RusLYkn44MbuAkiWjn2irHfPoyEWN1pqxn9AqYqzgcDxBUFRZcT6fWa92WKupqoqyDGQhyEBRlozjQGG2lC9vvHkeydpSNWvyoFAK0lxgKkmIA8GFhcArDT7MnMITITiUKpjmMyk7cjI4P3G5ufvc+vqJKHIpJbuLLW/fzhhVczqMVFcNVQ1CeNa7LcPwTCIzTp7D4YBSkrkZKYsVfvSLLctYVm1LCGecf8YUQKqY8oCIsGlLnIe7y4K7D17xne//CX/w7v8iVg9cNh+x3dZMYyTH6iV+0FOXK5JfsoJIgeAdf/QHj7x/7PjgtqFt1mhjsFpycWeYeGBVbZjdyDCd8D6/aFkSRbFjGBYjs5sT1kjGbiLmhNSB88kv1K75xJsfPFKYmu3mik/ffJ+2bRDyCHiuLl6DCOz3TxhbYVRJc13y/PxACgKZN/TDCSVBpBZrVmi14I7dOFLdlXT9nqpqEDowhxmyQgTNuT8yz4ahe6RpNsQsaZotG6N48+5TfAjM00hRFKw3Eh/OCFNTlIpxPKP1jtP5tEhiDwekkrg5QioQUrBd3fD4/IZVvaWuGgZ3RMiBZn2BtFA2BiXbZak2wePTO5ybqeqCnD1KBYKPzHLBz5XmEjf9FCAptFYMw4ybIq+uv4zhParwlEVJ8IHT+UiMDh8jYx/pThktJXF2lPURaxbcWdXUnLsTxoyEMOJmicwJfGTVFIR6prKWv/lLX2V/3PO/f+ufYS57YkqcuyPDcMCYhrre4v0RaxukUpTGUhUVz4dPGbol31JJwd3VJWVZIJWmWRcksUdrRSbQ9We0KpjGRS/inOfMRFWuSUEgEnRHz2azJcRITBnvR8ZhwjlFjILSaJSsGXuF0csbr6wK5jnSdfsFEW0SpliAoFVt2DY7hj5zeJ5YXbas1yVzyLTtorIsqhNVpZhmgTYZpSpicEtEu4ycjxmBoyobVu2KrpuYw4iPmouLNcf99MNQgWlylGVNTpG+i9iixj8P5DzRrEqwkjAtbzWhEjGGJYhMLRyVTEKyxDcKk6m0paqaZe4eEv0Q2Kw+IKQFxKRU5nTcY1RNioshW0rB8Xz+/Pr6K6rjz30WY4Hg+rYm5oHdVYufZ2ph2EdPyDMhJvph4rT3i6NeaEQqiM7weOwoS8vD/UBdV6RuIEex3MxlhzYtupB8cLPm9XXLdrfh9//8DxnEmTWSw/PEehXISTIOJ2xh6c49V9dXxDgxi57ZTXTzjKksX/7iLYfHju2mBTLGFphaI5RGSM3j4yNkScqaaQSpJA/ve159eMXxfMLPkrLQjEPi3f2Rti0xumWaTmgtIZXU1ZqyKBjGjtevP6DrRpSQWF1wh73KcgAAIABJREFUOvXkJLm4vlyOuwzGCnbmGp1XaLPn6z93g1SClAJX21fE6DmfezYXDd4NtM0Fs++pa8U8R2LI+NwzTUfapmWz2zJOZ6LIxKBYNRXOB9q65tjtMTYSfCBZzTxHBC3v9yeKQlMVkhQdkw0IEWnaDafTnra2zP5+uR/JRSbc9xMhJooKQnT0ffeSTRpQqiKRWLd3hODoO88Hr78M2fD4/KeLHS/uMWr1ufX1E1PkRaGRymAKST90SFFw//zMYTwgZEGKM7bQSBEpq4rsNS4OTH3PanWBmz0RGEWkP08MfeLy6oIQ9uwaS6ULvvKlK+6u7ziOjkOYqDcND49ncrKkKHBzRmlBcCeELJEyce4PCCEoi5q+69Am8rd/6+9w/8kD3eEBskApUHXEM+LniRxrfJjARHIW7J8dUtTsnwLTIDjsHZsdKJUwRjFNnnonGQZJzhlrNEVRgkxIaVmtl1CtrhuWeHAXWK8r5nnGlstMffYdOQ5cXzQoEoUR9OcjVm8Q0uPmEylPCGEXEGkGISuens7EsPDEEZKUBMM4ou0jbhI8PY5cbLcM855xCGjpsabEBbf019ISAjw97tF6MW5HP3PX3ize1PmIYMKahnHqECpRVw2n8wEfPGHOC7U3JbwTuCkjRUAoQ1WsmOaBvhvJSXE6DoScGYYD0/iEko7NzTVl9VNQ5H+BP07Z8/D0Kca0jL3neO5IWeG9p1lVuHGhxha1hCDIRA7HkWnukWyYXzjV5/MZIQzOOVbrDdM08PNf+JhvfPhVdLPhz/7l7yIMKFWy3mSMFtRVgzGZaVwCupqm5HR6RKnipcB6UozIokCXki9+5at88meZ4fSMqTTjvGhuRDI45zHWcho83gvGIZBixtiSstSs2okwR8q15XQ8srkwPDzco5Xm8u6SwlQM80hVVYxjXsKtSLSrjFSRurFM00i7uuD5+TN8gLJQBOeoyxOjOzG7maa6YXY9j0/P5CwRSTBPEW0DORWEOTAMDqsNKEGIBqUi24s1MTiKUvLhhxecjh1hFGilUUaCFNiyJome5AUxJrabDVKBNpLj0fPmzVte3dwhc4WbI0pHZtdT1Wv6fkbkEjcnVNIgNdMUGEaPEhltNHV9wenkkELTTzNGS6RekjrIinVzR4gzwzixP33+Wv/z96F/RY8Qktn1KGmZp4yfBdPoSQmsNTS1QoqAFIqcFtjk7B1d55gmT98FIFBVlpwKlCwpi5qyqBEYbrev+eVf/Ftcf/mXEduvsI8zo3ugqhbzsJSa2c2Lj1EKxskxu36R67qJ8DIaCzEvuZMZtCkompaqWRFy4vH0wNBHnp7OpCRRqkCp4gXgUxJ8oq7W5AxG1ygpEBmk0BSFomky69ViAeunezbrLV03MI4Hnh8f8X7JIoKJafKENDOMe+YpIrA8PkycTiP90HE+jniXeH5+z37/zDTMHJ9H+n6i64/0fc8wDFhlmacZY4rFrTMtken750e8E6QkMVZjConzM/5FYlE3lt3ukpwkzvcoGWjXhrJW1HXFZnNFipLn5ydurm/JL0l6ORcMw4g1LUIuo1KpNLv1FeM4kYIg+IwUmqendwQ3Y7TB6OWtZ40hOsnpeWa12VC3LVJWfHD1M59bXz8RJ3kIDrLmfD69GCCeWa+uUXJBw6WUICe6cVkkVPVitNDaEPxMVRiUjGir8CiKKnOxK5ncmdbs+K3f+gfcfPANUhJ8949+h6fTG1QB03TCaMXxdML7xdwAASHsEnylK87dEUiMoyPFTNefqL5+SXaL+k9tL+ndE26f8D4zDYG20QQfyElDspy6hQuT04JWcN5ze7MDEalXBiFngvdLXxomJAXnvuPp+Ym2LimrmrIoIIPznpwmYoooGZFijZs9ORX0Y09/VuyPi01PqkjwAq2XyBqyIefM02PHZgNGlaxWO2YnUQG0rnl6eL9wHP2ZaQrcvtrgvX9Z3MhlG6ok5IZNu2Y0C0L6dBopK8ucznTnmfWmxhaa58MzdWW5WF3w/XffRomCWWk2F9cE954QBPM4IpC4uaNuSjKZ82Hk8qpBiIm6VUvyclIoPfHVr93ikycEiCHx6dsffG59/USc5ORlIeRDRJlymavOR5p1g1SAmCkKS1m0XN+0BL+8vrybsMZwd7tj1TZIoXC+X1w9ccLaxFe+/At8/I1vUlQtx/ORtw8/YHQPaCmZpoGn/SNd1yGFIgSP9wHQTGPP8fhMDJ7j8cQ45OWb6qDQFdPUkbIHazBVgzUVfg5IWbBq1wghqaoKayty0oQX9npVWspyCYLyIZBioLCSoo6cz/slC6h39F0mJ4WbIadISoGu71HSkFONFBUhQFGU2KLGGEldVyizjAPv3w1ouWa1uiB6jRDLvxn7BKkmBMn7h0dSNJRVSVFaFu6GRr/M/a21nE8jQ5+IMRKCW8Ro2TH7M8aW2DLRj8+UlaBuFNYqikJhTGacliSLsihp1i3r5oab29sXg3JmtVpzOg80bUtTV5SV/r/be5dY27b0vus3XvM912O/zjn33Ft1y2XLsYVisKzgCJReJOR2hEInkaAFHWg6QkKihaBBAwkpIEEPoYBAIg1QCJC2HSCJMYnKqbLrVt1z7jn7udaarzHneNGYq8wlsi+UyfU5t3T+0tIec8ylPb+99rfG+MYY3/f/k2Warrfsr2qKMqJMRGdgctjsc168vEKoVdljrbhSXN58A3Q8UxLryBci0zJgtEZIiRIJYqLMS5x3ZMWqi6kzSZ7vCMGz3dWgVpYrnQx1FRFkZKWgKa/5i3/hX0VJQT9ZTqcDj0+vcH6gKl7QDT3ez5yOlnmSbHeShGKylhQ187yg1KpgVuY142T5zrd/BR8DsxsxWnP/+ES9y7DTRGYqVAEhrTsPSjlMpqnqEucT11dXpOjWkZCVntkFT5HtmGaw/sRoPTFCriPTEKkuV/riyc4UxVq5EwlUpWYYPBeXGfMyIoUkiIC1E1pLLi73aC0oColb1nVF3Rik3DIOBxYbUKlCohEJpmlEirSKg82JvAhkeSIliZ1mQgwMo2OzqVhmx2wjs33g4rKhqQNSCdpWEEKGD54YBGVZgJgoy2ukDDT7imVyVJUky9Yv6Dw7dC4RViKFoak2pNQR48JkI1VVYqcJHxNNtV1VKpTB2ZGi1HgXscvxK/3r/RjJgRhylilnGiPWJqwN2HnB+Znj6UhKBikMp+NA3RQIKdjsNpSVIc8LpMqxS8dmU1LWhuvLn+ev/Mv/Hhe7lwTvWdxM13c89a9ZzyLBRw8xZ799zvX1Zh3FkyCEGSVzlGyZrYSY0/eeMr/m8emW3/57/yMhJpbFsvgZYxqEVAgZCMEx24WUNMGnNdvu8oKrix0iReo65+JiS16YdQdGLCA0hJIUK7wTLLPh/vaeqswZ+pnTcUIkgXcL02hRUjLZQF5UzHah747E5MhNixCaq6sd19dbLi8vcLMFPJeXLSDPMi8LZZnRNBn7fc009gQfKHK9FmW3Nd6BFAUCiffrGYbA4L3AO4OdAqfTCUVJlkmqCqQMzMuq8hbiTAweJSX9cEQkKLRi117gvAcRVu7GQqAkeDfTtBUxBcbxxDJ7pND0XU8MoARYu5Diqiyhdc40WKztUeqrD4PeDyc/V/wYUwI5ed6y310hJcQgAIP3C0IFLq72iFQwjpZpWhhHz2w9KYU1zFlGhuGJbbuhqfdAXA867MShe8LFjqrSa6qqHVEqYTKP8yMhaJSszwujVaRrHGamMVHmLdN4wC0nCCWLX1Y15WHg9ZsfMU9hpZEQmmEYcEvCTg6BIQmIacEYhQszj4d7fLD40CGEYBg7BntCywLnEsu8VhbFFBDCoFVJxIBcldWCX/CLZBwTXTchWPN9hJTUVUUIM1kW8GGkahqquiCm9bNM0XJzU+PcRJZrkvBIBU2zhiyXFxtMFtEyJ4aEkHB1dYlglUsfh5kUFRLBptoTnaXJN9hxWWc+Kdm0NVVVsGkz8rJg8SNSaXzw+Ohom4Z5XgBFXpTr/0EKEAuL79nttmhd0pQ7clUQY1oX1f3AMk/E4BiHjhBWwbH7+3+KNZ5fF5TSK+l6m1NvNhi9EmaKJIhlIuGQsqAoDDF6pDBIWaw5EnkNMWLndTTyLrFtr/nF7/4La0rr7Igx0g09b+4/483dZ1zttxy6A5neovVCiCMxCUJYeQ+VLNc9XTxSmjNJfiDPS4q8YdNWK8VynjEtPX7MkCJH4kgxMVtLlitSivhwRAjQSnDsb1HKIGVimQMuLDjvmEVc5cS9Z5w8RmvKZg2dupPl4rLGzjN2tIAmxIjJNHZav9wrgZEkyxTOT5AkdbHhMNwRvESwkGUVIYzkxRpr22n9vA7TI1JJQvRYa6mbApMZPv/8yOW+ZRwtUWa09Uo/fXx8QDQzL59/xPFw4ulk2W4/hvRI9CAoGSd7XkNlBG9X5Y6YaIuS3jmUiMQI49jRFnuUztFacP/4iv3FhsWuEikujOw2ex66w1k5T68nwXnOZz/+jLqpsXbAh/+f5EJ/GhACur6jqffkuV7L1/qeTV2RZwKpK4TQhJAwxnB/94DSEq0Vw/hAbiq8jywzbLeX+ORIaIJzBL9wd/+ah/tbvD9SljneR8ZxoSzPFUZCIMS6VTX0T2SmZrYDYMizbM0edCPBB46HR+4f7vno+Z9hGgdm35GWcpVAdGHdyQiScbBkWc4wDGgdKXKFUKvKG2dm2xB+klvuUWLdsqurHSFO+BiIPtG0FafThFGKyS5keUbdFCzLTNNsCcGiVGRxEzLIcw5PiXXrsXueFURviCFRVRVKZizLiFEGRERKw7at6LonyqohprDq95SSvAzEWFDkGWwLTCa5vNnSnXqaWiP0lqqa6IaBLGsIcUBJyExGDJFtc0E3HVGyop9Gvv38Gfc//j5KCvziiIsgSsEcPUXWsK1frgpzy4mi0HTTkcGuYY9WW1LyuDAzTR0fXT5jcDNKFOy3e+D2j/Wv98LJSXB1dYVbPKfTHVIZdpsNEOFMPTHPA2VR4FKkLCVt2/J4eEsIOXUpsfaAyVqaesNnP/whU/fE08NrJjuQQmIcToSwqqTV50IG5yxCrbkTErkKvw5A5SApvA8oZRiHgRgDKUailxRZwx/88PeQctUYWpYTzk+4IICVksK7gHMTUiqEUFi7IJVY4/boV7apZPAu4l1E5Zy50T1SavyS8G6lnY5e4b1nWQJFCaBwbibTMzG483Zngw/zGtYJTYwnMlNgTM5gB5xfK4uSlH/IvRhiYLEzpIaqLDBKIbVZxWMLmKZlPQ/IBXcPb2jUjlPfs7/KUDpRS0lb3/B4fOJqf8Pbu9cMk2Wz3VKaFikNSudkWQl+zcdRSiHiSuPRLSM2dCzTwouL764Zj+FEmW9IIiKkwdoRLc9bxWWDCydkSrTtM57nW5RMlFnJSqz8R+P9cHIhiU4w2QGtC+p6zzA94f3CvCxsN8/OleiGcTgQUuLh4YG6vsDOE6M9kOclUkr6/sTNzTU+DDw+fIGfLT4ZXr36PQ7jFyi1Cl9Jada42iW01PRjj4gZH734lGF6ZBYepSTO+/Uo3YMdI1Io7m7fIHSiKCXJC1LSSJXItMXomqQUw7jym19e7oCE1jnDOKC0oygLlmUNNeZZIFUk2YVFrL9LqURV1RSZ5NQfcD6itefyaoe1I3Ya1+SvZSIz2apMXEqmaWZZRjIT1i3NEGlqiRAet0S8dOeZwpOCYXETdVWs6m4ukCHO5JkJrUEKdZ5ZLSaTaJGR55pp8kycqPISu1jyIsfOC1V1RfBHolvo3UTd1LjgsfbEZfOMKBJ1XrEsK4d6Va/8OKiI0AtluYdxYpaJJCGG9TDIuZnNds/QT0DgYnPF4kacG2iykuPhzVe613vh5Cmep1u55k4MY8c0WvKsoCoz5nlAyMQ49QzTSFE2lHmFnwfKvCbLK8bhESEleSmZreWzt/8QRs2rH/0+3/r5X+Ku+x7D/JYwB2SZIK1qwllWUhQtp84ihWewB7TJmENCEtBSk6JCG8PuYsftmzXTcLcrQa4FzEpFQKGMoSwF3mVrwcOUUdcldlrVIJx3hCAJwTGOFqNzdBZx40KWZZAWpIxst+2qW18o8iJHukhVZeS5YhgiWVbh3HzO4gsYVeCspzItU7fQjxFj1kWed4K2NUBYc11UjRI5iYQ2Fc22waiMbCnJc4PUGafuwOQnmkYSYsAYRVXVtHVNEjDPJ1IKJKDve3b7a4ZhoMl3bJ9XvL57jTLjmlJsZ+q6oaoMj6d7qnJDCAt+CWyqHbO1EAQ+KQZ7oMwLZreydSlVroUrWb3yq4SJfXtBlIpTf6TJaoIQyOyfHj/51wchQOSEkLCjo6wUeVGQZwajC24ffoxWFXZ2xChxLrFtdgRncKEnekWuYXI9WjWIPGcYb/m7P/zvOTxF/L5jiSdAUsqK0a4a8EppxqFDiYZ58lxebYk4hvFIIlKVGUJk62mcE1xctETvV4rh/gu2mwJrHVoppEpAwTSuCsdSJfb7DfPi0MZweOpAVIx2QCpHSgafIlJpjF4rbECQVxnOOrRReL8KQGkdKYqSvu9JKeN4nHFuwGjJxERRFDjnINcoUeKWgCpy5iUwjQtKJpo2J3i7UmCokqyQiAgg0UbxCz//HUDz5vYLlJRIoYnB4AGtVs4TgUHr9fMvi5Zx6ti0N0gh2OwaSinJjOLZR9d03appVFcleZ7j/MTsFzb1nljvOfpHFueZFouQGhcjhS55dfsZ+81HKFEgokJKw2RH5tlT5Rseu+Oaw68KolR8fveaTVl+pXu9F1uIUkgOhyemcUYKWGwgUzmZySjLDVW5J8vX8jKBZLYLbVvhloARGVmeMy8OtzicXesyo4988t0rmgvL4fCavFAwRy7qhnZTI5GUeUZTbIkBnt9cU+U5ySeU1BASN9ffoigk263m5UcXbDYFv/7rf45PPr1mu8mp8j3b5hlFkRPSTJHnBLdyIUohcItnmmasdWSmxNpA8AYwlGUJAgQBvwS0MjR1QQqW3aYkN4lMFVRFRVPVTL3l4XZi7APzOJKJjI8uXpB8RvSCFAR+8eSm5PrigsWeY3sfiVEwTxE3afCGwmR4t+B8wC0zXffEZz/+nGPXYUzNZntFUdRrgTWeGCSXl89JeCbbc3X1Eqk1++1H5HkG5xJ0ZUDkjphGmrpEa0lVZSQcbdWss47MUUpRlA3+J8zfQvF0fCRFyLIaCGyqLXmm8Etk6KeVUU1ptC6YxlUM+O3tK7quZ3bfiPK3VXunrva0dUNZ1lg7I+RZ0loolFYMw4lI4uOXnzJ0dyiRGAaLzjISkquLj5EqsTjJ61d3bLeX3Lx4Rm4MBMWn20/pwz1BRx4fFTe7a0JK2CWxqVvaquEHP/ohCM0iMvJCYq2mLQsQms1uz/H4xOtXf8DzZx+hdM7rH32GKSuMrshzzctnH3Hf3VLWOfMUyIwiOM3Rdmy3LcfjESkEWq8FC2VeIHWkLjPmaWZ7saPdGJq4Z1kUx+6RTBvGKVAULVVRIEnsdg3DtHC1e8HD4TUoCD6w3eQkGfBHx+5iR4yJFDxtVRHmkTqvaJuC0UumcSTTkjwzPB1mHmPH4qe1sHp8pK5qqqLEjuui2doZQcE4WbabLZnOyWTGOK+sX0l6+unEqXviYrtW0++rHa8eXuHLwNXuhskNqBQRckFJzc3FCwD6pccuPUXWoBX4EFFJnRPYWoqs5PbunrKqKLKSpqo5olAm4vlq6ub3YiQHViXiOGFdzzAcQQqWOWAyjdaK2Y7kplpPwcYn7BJIKLQpAMHVdk/RZiQZcX7h8vqSp+5AZWoypRnGB9rLLYXKKKjYX9R8/PI7qEzStILLy5K8EGx3FVWRsWs2ODdyc7nl8uIKoRUxZCTv+ejZCxY/ktzEbrulbCRtU6wKFWVJVeQUuVmpmwFlErtdi/eBqsowJiMEyenU4/2qnnD9rOLmJufioiTPJXVT4dwqcBsjNHVNXRukStRtzv3jE9M08fuf/Yg8awhe4BNoI5Bi3cZ7/nxHZgLPnlW0VcbHzy949rxAmpmmNnzr5SVtU9J1HS44Ftcz9Pe0mx1ZnlPWOd45Nu2GcewQKqC1xGi9nl2YyOQ66qohRItIiXFYECmn7480RUsMDokiJUeTb3B+QkjBxW5PkRnG5Uid1Uxjhw8LkYiPisPxgLULMUQEEQU8v3oGMTGOHZGFuq1oN1uGb4KOpxCCJGBxI1oZdJD09okylwwnQ0rrlh7os1R3QiiFzhTDcWSnWmQusf0a913ub/ji7Wua2LJ/8Ut89vZ/ozZX/MGrHyBlwmhD3bRkSqFFIq9LnB8BQ4oLRa6pKoNLibuHB27vjuisojQjdZHTTwM+zWy2LaaQeAIy1Yx2IISAktCfFpaz/IfWmrIoifRkmWSxrIlFuy2L68mzuJ5WtiVDP1BWJXVuyLQgBU1Zropnzgu8F0zTTPKCi32Lkhn3d48En6D2LHairko+eXFFriOX+4bttiLPBQoD0jOcemRc2LTf4vWbzylKg84TJsuoyhuOT294dvMJD3dvaduWsm4YpxOgcX5EaUdetHjvGaaOi/0l1p4odAsciTFyOh35zrOC4zxBWHPEldE4b+n6juvLa47DE7NdF6xaZszzjBAJYxT3j4988vwTbDeilcLFVc9z17S4FOj6jjw3jNOJJL4JhJ9JsMwLZVlRmILoPcFbjnZhNgt1XVDWO47Dgf3+inmZIDrII02xQSqzyvoJySavaeoLbnYzpWiY3cjj8YAtNDoq+vmRm90LPtp+m8T6odqxJ2U503SkKAwmNySZ6B8WHh46DqcJrQO9XJC7a+4OdyitMfJIs61QUqOkBLtyITbFJfPU0ZQlOlvpMEJY4/T9fkdcPGWVg4oMp4ym0Uynke3lhrbJGKcBnY2EJRIXjywtujSkSXE8jJyGiZtdy35XEUQgL/YkK1jCglCKy8uGV593tJdr1mZRFszLidN4CyEnUxJpBPPSUVU5VdkQoz6raxzQWmFkxrc/+S6PhxMPD080TU7Z7MiLDsG0lgmqLSTNm7vPKU1JP4zkWbMqUZwO5KZkeXrDVfOMGAOn4fh/n0xPgeA8VVFznG7JsnylwpAJFyxFkeOSYxUdlAzjglJ+JZSaFqJc05bLoiGFb4COZ4wBJUsWC7rMEbnjZEe0MZgsJybB/eMdNkzkLmfTXGDMyvA0e4deLP3jCe881vb8fJZhh0SWBX7vs99BxxLvLX7S1EXLp9e/yOvuc3JtCAEyvdYvOhc4Hg7owmC05PAQ6PuEiBlGK27fHJA+w6WVLmHMNIFA27ZEGVBEfJzY73aoXCOSoB+OFGVFVZRI6dg0GbvdBcPYsSwLzz69wvYLOE3oIs2+5e39Ld3gceNMlkuauqKz9hyPRgQCmecI4/jkescYBi7aDeM5L9tHhy4iVW3QWjD0R4x25LrAe0VVViQN0mgaXTNNgdn5s2jufhWUXmbuTx2nviMvBJkpiGEgupEInLonmnKdWYw2LL3ndOwxtaYwGS9vXrIrW4Zmj18Ci12ZeIu8xkrL4/DA1e45h/GRGDwheUyeYSdLcBMvXzxjmCzaKAQSESWJABK0NEzLyOwXljkRov1K/3ovYvKUwI4zKRre3N4zOUGKksvtc/rBEudErTdcVy+o1Y5sNpRZzW53TakjG9MilaFqKspyy6tX95gmJ+qE9AItDJnMePHsObksefv0OQ8Pd3R25NgPLCnwcFjzypWRiBQZTpY3bx/RmaAoNc+fP+PZzQW3DweGPqxlcclT1w0hBKZxQmnNbt9QZjmud6zq74n9tiY3kdIIri9aEAtZIcgKMHlJu2l4dn1DnRW4Zebi4hlGZ9SN5vJmR11ds9ltSUnxC999ya/92V/kcpfRNhVaCT6++Zi8lEjjUPmEKiLbqxo7DDg/MdmOKCSzC0yuoypaQNGdAsMIp25m7C2FLvHBokyFjfB06hBqwfuOfryjG45MY+B4eMKOMwpFnRUoBE1doxQwecI0s2s23HePPA4Dkw9olTNMjmGcVpW5sUNLRfIBIw0hcq4XXSmdre1IwtG0NSaDoigoi5pxmhmmiW6YGKflTC/4DaCkEEKw32xJQvN4/8B867m42NAPHu8SS0xIzcphPQiexgMv4ifI3cjsEt5pXuye88XhLeM4024aHg+3XG8uUdlEShk6z7k/3ZJ8IB5XCuinxze0m5bj4YmUJKdupt6CFhneaS6uWqx1fPztPeO4cHgcqKqMaiOp6oqyFPRDR991tO0WYQwqeq7zmm9/1JCkRRSJcRiQQXCxa7G2pyhbsrxgWTriMuPczMkOlFlNiDN+8TQtVGZHs92hSfTHAT8n2JQELdhd5Hgf8Gnhi/svKFoILOdtuYoUZ/LKoKTGmBIfHaPt+PTlL2GEYVtc0A9vcct5JlUarRuiKPns81dUlUbQMQwd188+ws4jfnIIp1Emx44dYeNBaUb7RNc9UuV7sHDfn1ByZOaBY99xs3vGYI9kuqbrFlAaKQ2nvl9lXVLATjOzPZFigLSKGqQUiSmw3ezph7UUscwbohsxRlGbBuciPnwDYvJV1lty6gaaTcObN7doKamrtYzsOI80TUOaFGH0fOvFd7i/eyBPGtM2HPojW1rCHLhodqv0iZJM88ypP5EXO0I/cjr1NGXLw+lz/plf/hV6+8T93SNjf2TTNtxc3DBMHYsQbNuaOTo2m4XoDCkduLjOMaqgbBVXV1seDyudxDI6nF0o6wI7Oz6/f42MqxCW94kUBJUu8WJdAxz7HqkCRbZm30Vg6UbqtqBMFXmeiCxkWiN0oOtWbcu2yfEx8fT0motdS64009TTNAXj1K8zS/WMaU6UTY3zlkwKrq4/ou9fc7mtEBQI0xDiwjQNBK9Qej1l/P4P/oCizPBhIMWMoigQKTLbCe+Wn6SVMTyN7NotRktG29NJjXsaAAAJJklEQVR1icV2/MovvOB2HHHekauC7mBBK5Zp4jje8d3v3JApxTJ58ryhGyYudxfcP97RjwNuDmx3LeCwbsHNDm0Kjv5ESmuOkfcz2/0G1QtSUqjkMeEbMJKnFHh4fCShsPOBtslxc2JWHlAIrelPI9Em6sJwP96RbSvG00yaJwIzBEtpVjqyptmx3265P3wBosQ5R1HV5KVmiRbrR0bXI4RGRMOmecbzF1uOh4ntdrsSj2Y5ufcsy8ToRsrKINRCU2mk0nTjE0KuanLXNzuGbma/2XPoHgiT4v62o7kRfPzylzjePdANB4pC4ZZADJDCQhKaEG+Rqebm+QWzs8Ro0EaTfAStmG1gcQAryf3iI3mZE/wMQHCOTFfINud4mMhMxMeOp8Md281HfP75gZAeUBI2bcYwPeDcLUVREEJcucXDykZQ1xkh9Oz3FW3V8ObuEWMMmaoY+gEQPD10fPvbN1R5jhAFRamYJ8HmPLMM88C+2BOdQQpLqTPqquL1w8I49WSmpO9mUrMqezwcHrHLQvCw3+0oKkMUC3hJComo1vSLFFeFCoGk609oLZimDpBUxeaPdy7eEycPMSF1hrMzWbayZsU4E3xGdzwRPGQ6Zxwt4mpHWBzj0wOFKZFHMFtHeXFFP3YE4Xn78Go9fVw8MUmQgaas8XHGLgvXl3tO3RPDONJsFYsD6zyPpwd89ASn0CawqVoKUVPmimHuWWbP03JHVe0QItFUN4z2LZt2Sy02JNYMBakk7a5EZ4Lj8AqnNuRNw2IfWObA4vzKxV3f4L2j659wydN1D+TZFu0NyWuyrCGGZV18iYBEMo2WzWbD8XjLtr2kKAXzspAyR1kXKymqt3z0fEfwPS9etNj5yPE48/rNA0a15MYwjpYiaxn7R6bpgFSGeVnZvuwU2LUFRdEgWb902+YFUsHFVpCXEjtO2PnAMAy0xZ6YFjq3cHG9IYmVnCkvDXaaGZc3CCXpx5HuNLGptyw4fPCklNDKUO1rEgun/g4pHYVZtxWDT/hlTdZSmeJ89LAycWlFcBNNdfWV/vV+LDwjDN3MMI103cCm3hA9vP3iluAkyxR4ejqQVxnNrqWpd6Qo8UmQ0GSp4vF4R6YM8xgY+5nbu1v6fuJ4OBB84jQ80Z0OuHmlCT51RwIeU2hMHjl299S1oTtaRDK4JeGZGONCiBKiYBojKWZ0pxHIsPOAEIqUDMpAPwzMNnB7ek2zK1kWjyBD6kjZtghpUConz2uiWwupQ1Agck6niRAVkx3I6wqdZyThMCaR5asuZjecSCwQPUVRElNPXp4158cDQoBPDjsvnPqBp6NlspFlUbS1pK4jZel5+XyP9ANv3/yY/uSw/VrsIWTONEiOB8+bt0emAe7vB44Hz+EYkKJkHByPD5bD0XF46gl+nZ2ejiNf3N3x4y/e8uaLB6JIHMaOH715zeE4sq2uOR17tpv9mmqsFHm2khEVuUJnrCkE0WNHRz+O68wWAvMyk1Ig09m6Nz+e1nOBJIgJHg7fgMogBByeerabiuuLHUlEPvn4Jfe3PU9PJ7JMY4xGJMnv//6PkXEdLZPoOTx58qqmMpJptsQkmefIF2/uUUZR5IbpFJm6R5LwLH7CVBqpLFm2ox+eWBaF9xGjI9/97rdY3MLjvWOeI3luUbIgz7fkWYQoUVoxzxZVGIbJIuWAxBCDxk4rKdCxO1GUgsUuSCNQusboDSktyBAQUVGV7ZqzgQbpqLItKS3keUsMD0QSfplxYaGq9vT9LU3dYq0lIjj295TmEmki3muMUfTTiclayqwkMXM4WoZuWVNqsz3dMPOjV2/YFDlCKE6nCV02LLPl8fiEUQVFvuHxocOFNad9siNGK/rHZZVQFInuZNlt90zWsiSHUhp3XLi5/pjJed7eP5FJxS674ObqmofuuOoJlYGYAt4uqwDZHJijI4wepCcEj5Ca43FkmWcyk2FkTdtsmd3CNC5IrSiKGilXiujMfHWClvh/0ZH9U4EQogO+967t+ClwBXz18PH+4JtkK/zJ7f12Sun6j7rxfozk8L2U0q+9ayP+v0II8b9+U+z9JtkKX4+970VM/gEf8HXig5N/wM883hcn/0/ftQE/Jb5J9n6TbIWvwd73YuH5AR/wdeJ9Gck/4AO+NrxzJxdC/EtCiO8JIb4vhPjN98Ce/1wIcSuE+N0v9V0IIf62EOIfn3/uz/1CCPEfnW3/HSHEr74Dez8RQvwdIcQ/FEL8n0KIf/N9tVkIUQghflsI8Q/Otv675/7vCCF+62zT3xBCZOf+/Hz9/fP9T/9ED04pvbMXoIAfAD8HZMA/AH75Hdv0F4BfBX73S33/AfCb5/ZvAv/+uf0bwP8ACODXgd96B/a+AH713G6B3wN++X20+fzM5tw2wG+dbfivgL987v/rwL9+bv8bwF8/t/8y8Df+RM99xw7154G/9aXrvwb8tXdp09mOT/8JJ/8e8OJLTvW9c/s/Af6VP+p979D2/w74i++7zUAF/O/AP896+KP/SZ8A/hbw589tfX6f+Gmf9a7DlZfAj790/fm5733Ds5TSF+f2G+DZuf1e2X+ezv851hHyvbRZCKGEEH+flbzwb7PO5IeUfsJP8f+w5w9tPd8/Apc/7TPftZN/45DWYeW925ISQjTAfwP8Wyml05fvvU82p5RCSumfBT4G/hzwZ77uZ75rJ38FfPKl64/Pfe8b3gohXgCcf/6EQvW9sF8IYVgd/L9IKf235+732uaU0gH4O6zhyU4I8ZMUky/b84e2nu9vgYef9lnv2sn/LvAL59V1xrq4+Jvv2KY/Cn8T+Kvn9l9ljXt/0v9XzjsWvw4cvxQi/KlACCGA/wz4Ryml//BLt947m4UQ10KI3bldsq4d/hGrs/+lP8bWn/wNfwn4X86z0k+Hd7lIOtv7G6w7Aj8A/u33wJ7/EvgCcKzx4b/GGgf+z8A/Bv4n4OL8XgH8x2fb/w/g196Bvf8iayjyO8DfP79+4320GfizwN872/q7wL9z7v854LeB7wP/NZCf+4vz9ffP93/uT/LcDyeeH/Azj3cdrnzAB3zt+ODkH/Azjw9O/gE/8/jg5B/wM48PTv4BP/P44OQf8DOPD07+AT/z+ODkH/Azj/8LjwWy7t1KBzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "\n",
    "\n",
    "#ap = argparse.ArgumentParser()\n",
    "#ap.add_argument('-i', '--image', required=True, help=\"Image Path\")\n",
    "#args = vars(ap.parse_args())\n",
    "img_path = \"/content/gdrive/MyDrive/Flickr8k_Dataset/img/1072153132_53d2bb1b60.jpg\"\n",
    "max_length = 32\n",
    "tokenizer = load(open(\"tokenizer.p\",\"rb\"))\n",
    "model = load_model('models6/model_9.h5')\n",
    "xception_model = Xception(include_top=False, pooling=\"avg\")\n",
    "\n",
    "\n",
    "\n",
    "def extract_feature(filename, model):\n",
    "        try:\n",
    "            image = Image.open(filename)\n",
    "\n",
    "        except:\n",
    "            print(\"ERROR: Couldn't open image! Make sure the image path and extension is correct\")\n",
    "        image = image.resize((299,299))\n",
    "        image = np.array(image)\n",
    "        # for images that has 4 channels, we convert them into 3 channels\n",
    "        if image.shape[2] == 4: \n",
    "            image = image[..., :3]\n",
    "        image = np.expand_dims(image, axis=0)\n",
    "        image = image/127.5\n",
    "        image = image - 1.0\n",
    "        feature = model.predict(image)\n",
    "        return feature\n",
    "\n",
    "def word_for_id(integer, tokenizer):\n",
    "  for word, index in tokenizer.word_index.items():\n",
    "     if index == integer:\n",
    "         return word\n",
    "  return None\n",
    "\n",
    "\n",
    "def generate_desc(model, tokenizer, photo, max_length):\n",
    "    in_text = 'start'\n",
    "    for i in range(max_length):\n",
    "        sequence = tokenizer.texts_to_sequences([in_text])[0]\n",
    "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
    "        pred = model.predict([photo,sequence], verbose=0)\n",
    "        pred = np.argmax(pred)\n",
    "        word = word_for_id(pred, tokenizer)\n",
    "        if word is None:\n",
    "            break\n",
    "        in_text += ' ' + word\n",
    "        if word == 'end':\n",
    "            break\n",
    "    return in_text\n",
    "\n",
    "\n",
    "#path = 'Flicker8k_Dataset/111537222_07e56d5a30.jpg'\n",
    "photo = extract_feature(img_path, xception_model)\n",
    "img = Image.open(img_path)\n",
    "\n",
    "description = generate_desc(model, tokenizer, photo, max_length)\n",
    "print(\"\\n\\n\")\n",
    "print(description)\n",
    "plt.imshow(img)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BR3L9dyb-v9-",
    "outputId": "9562eac3-e001-418c-8c2e-65073b9015bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: content/models6/ (stored 0%)\n",
      "  adding: content/models6/model_6.h5 (deflated 19%)\n",
      "  adding: content/models6/model_0.h5 (deflated 18%)\n",
      "  adding: content/models6/model_9.h5 (deflated 18%)\n",
      "  adding: content/models6/model_7.h5 (deflated 19%)\n",
      "  adding: content/models6/model_4.h5 (deflated 19%)\n",
      "  adding: content/models6/model_8.h5 (deflated 18%)\n",
      "  adding: content/models6/model_5.h5 (deflated 19%)\n",
      "  adding: content/models6/model_3.h5 (deflated 19%)\n",
      "  adding: content/models6/model_1.h5 (deflated 19%)\n",
      "  adding: content/models6/model_2.h5 (deflated 19%)\n"
     ]
    }
   ],
   "source": [
    "!zip -r /content/file.zip /content/models6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "Q2hA0IKv-Fw8",
    "outputId": "5e649918-bb85-4f46-9852-0f3376362896"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-19-ab05477e43de>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mgoogle\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcolab\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mfiles\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mfiles\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdownload\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"/content/models6.zip\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001B[0m in \u001B[0;36mdownload\u001B[0;34m(filename)\u001B[0m\n\u001B[1;32m    141\u001B[0m       \u001B[0;32mraise\u001B[0m \u001B[0mOSError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmsg\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    142\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 143\u001B[0;31m       \u001B[0;32mraise\u001B[0m \u001B[0mFileNotFoundError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmsg\u001B[0m\u001B[0;34m)\u001B[0m  \u001B[0;31m# pylint: disable=undefined-variable\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    144\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    145\u001B[0m   \u001B[0mcomm_manager\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_IPython\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mget_ipython\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mkernel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcomm_manager\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mFileNotFoundError\u001B[0m: Cannot find file: /content/models6.zip"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "files.download(\"/content/models6.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8OtIgjJr-mom"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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